# Best MLOps Platforms - Page 2 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Databricks is the top-ranked MLOps platform in 2026, rated 4.6 out of 5 on G2 based on 1,300+ verified reviews. Microsoft Fabric matches Databricks' rating and excels for teams already embedded in the Microsoft ecosystem, while Roboflow leads user satisfaction at 4.8 stars for computer vision workflows. 1. Databricks — 4.6/5 (1,300+ reviews): Unified lakehouse for ML and data engineering 2. Gemini Enterprise Agent Platform — 4.3/5 (600+ reviews): End-to-end ML lifecycle on Google Cloud 3. Microsoft Fabric — 4.7/5 (40+ reviews): Unified data-to-analytics pipelines inside Microsoft ecosystem 4. IBM watsonx.ai — 4.4/5 (100+ reviews): Enterprise AI governance with foundation model deployment 5. Roboflow — 4.7/5 (100+ reviews): Computer vision dataset annotation to deployment *Updated June 2026. Based on 2026 G2 verified review data across 5 products.* Machine learning operationalization (MLOps) platforms allow users to manage, monitor, and deploy machine learning models as they are integrated into business applications, automating deployment, tracking model health and accuracy, and enabling teams to scale machine learning across the organization for tangible business impact. ### Core Capabilities of MLOps Platforms To qualify for inclusion in the MLOps Platforms category, a product must: - Offer a platform to monitor and manage machine learning models - Allow users to integrate models into business applications across a company - Track the health and performance of deployed machine learning models - Provide a holistic management tool to better understand all models deployed across a business ### Common Use Cases for MLOps Platforms Data science and ML engineering teams use MLOps platforms to operationalize models and maintain their performance over time. Common use cases include: - Automating the deployment pipeline for ML models built by data scientists into production applications - Monitoring model drift, accuracy degradation, and performance anomalies in deployed models - Managing experiment tracking, model versioning, and security governance across the ML lifecycle ### How MLOps Platforms Differ from Other Tools MLOps platforms focus on the maintenance and monitoring of deployed models rather than initial model development, distinguishing them from [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which focus on model building and training. Some MLOps solutions offer centralized management of all models across the business in a single location, and may be language-agnostic or optimized for specific languages like Python or R. ### Insights from G2 on MLOps Platforms Based on category trends on G2, model monitoring and experiment tracking stand out as the most valued capabilities. Improved model reliability and faster iteration cycles stand out as primary benefits of adoption. ## Top MLOps Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,323 reviews) | Unified lakehouse for ML and data engineering | "[Helpful for Managing and Analyzing Operational Data](https://www.g2.com/survey_responses/databricks-review-13090803)" | | 2 | [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | 4.3/5.0 (654 reviews) | End-to-end ML lifecycle on Google Cloud | "[Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)" | | 3 | [Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews) | 4.7/5.0 (44 reviews) | Unified data-to-analytics pipelines inside Microsoft ecosystem | "[Finally got our data stack in one place, but costs need attention](https://www.g2.com/survey_responses/microsoft-fabric-review-12740895)" | | 4 | [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews) | 4.3/5.0 (53 reviews) | End-to-end ML workflows inside AWS ecosystem | "[Fully Managed End-to-End ML in AWS with Powerful Distributed Training](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)" | | 5 | [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) | 4.4/5.0 (134 reviews) | Enterprise AI governance with foundation model deployment | "[Enterprise-Ready AI with Strong Governance and Flexible Model Support](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)" | | 6 | [Roboflow](https://www.g2.com/products/roboflow/reviews) | 4.7/5.0 (154 reviews) | Computer vision dataset annotation to deployment | "[Roboflow Makes Computer Vision Projects Easy to Build, Train, and Deploy](https://www.g2.com/survey_responses/roboflow-review-12984362)" | | 7 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (708 reviews) | ML pipelines on centralized multi-source data | "[Snowflake Simplifies Data Management at Scale](https://www.g2.com/survey_responses/snowflake-review-12898129)" | | 8 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (771 reviews) | Enterprise ML governance with SAS code continuity | "[SAS Viya: Powerful AI & Data Analysis with Seamless Integrations](https://www.g2.com/survey_responses/sas-viya-review-11855145)" | | 9 | [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) | 4.3/5.0 (87 reviews) | Beginner-friendly model deployment with Azure integration | "[Cost-Efficient Medical Data Integration Backed by Great Support](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)" | | 10 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (213 reviews) | Cross-functional ML workflows with visual and code flexibility | "[Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity](https://www.g2.com/survey_responses/dataiku-review-13125252)" | ## G2 Grid® for MLOps Platforms ![G2 Grid® for MLOps Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/mlops-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1333204&focus%5B%5D=1308795&focus%5B%5D=52115&focus%5B%5D=125020&focus%5B%5D=10938&focus%5B%5D=1327283) Highlighted products: Databricks, Gemini Enterprise Agent Platform, Microsoft Fabric, IBM watsonx.ai, Amazon SageMaker, Roboflow, Snowflake, and SAS Viya. Underlying data: [Grid® JSON](https://www.g2.com/categories/mlops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=microsoft-fabric&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=amazon-sagemaker&focus%5B%5D=roboflow&focus%5B%5D=snowflake&focus%5B%5D=sas-sas-viya) ## How Many MLOps Platforms Products Does G2 Track? **Total Products under this Category:** 257 ### Category Stats (Jul 2026) - **Average Rating**: 4.51/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: Arize AI (+0.85%) - Among all products in this category, Arize AI recorded the largest rating increase compared to last month *Last updated: July 21, 2026* ## How Does G2 Rank MLOps Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 7,500+ Authentic Reviews - 257+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which MLOps Platforms Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews) - **Easiest to Use:** [Roboflow](https://www.g2.com/products/roboflow/reviews) - **Top Trending:** [TrueFoundry](https://www.g2.com/products/truefoundry/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### JFrog JFrog Ltd. (Nasdaq: FROG), the creators of the unified DevOps, DevSecOps, DevGovOps and MLOps platform, is on a mission to create a world of software delivered without friction from development to production. Driven by a “Liquid Software” vision to keep software continuously flowing, secure, and always up to date, the JFrog Platform serves as the definitive software supply chain system of record. It is uniquely engineered to power organizations as they build, manage, and distribute trusted software with unprecedented speed, security, and scale across hybrid and multi-cloud environments. As software engineering evolves in the AI era, JFrog’s newest offerings address the industry's most pressing trend: the rise of agentic software development and the hidden security risks of "Shadow AI." In response to threat actors increasingly targeting developer workflows including a massive surge in malicious open-source AI models and infected packages; JFrog has expanded its platform capabilities to deliver absolute end-to-end visibility and automated compliance. Key new innovations include the JFrog AI Catalog, which enables organizations to centralize, govern, and control the lifecycle of AI models approved for enterprise use. To secure autonomous coding environments, JFrog introduced the Universal MCP Registry and the Agent Skills Registry (developed alongside NVIDIA). These new solutions establish the industry’s first enterprise-grade trust layer to safely manage and store AI agent skills, monitor connections, and instantly block unsafe developer tools or malicious coding extensions right where developers work. Furthermore, the integration of advanced DevGovOps and Runtime Security tools allows teams to replace slow, manual compliance audits with continuous, background policy enforcement. By shifting security left directly into the binary pipeline, JFrog ensures that the volume of AI-assisted code does not outpace an organization's ability to verify its safety. Today, millions of users and approximately 6,600 organizations worldwide, including a majority of the Fortune 100, depend on the universal JFrog Platform to eliminate point-solution fatigue, bridge the governance gap, and securely embrace digital transformation. Learn more at www.jfrog.com or follow us on X @JFrog. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1910&secure%5Bchosen_at%5D=2026-07-22T12%3A24%3A37Z&secure%5Bdisplayable_resource_id%5D=1910&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1910&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=143017&secure%5Bresource_id%5D=1910&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=llm_category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmlops-platforms%2Ff%2Fmodel-monitoring&secure%5Btoken%5D=60c35b6c36dcdaa820539accf8689ea6e364d5eadea3c3ee367ffb5b2fd76410&secure%5Burl%5D=https%3A%2F%2Fjfrog.com%2Fartifactory%2F%3Futm_source%3Dg2%26utm_medium%3Dcpc_social%26utm_campaign%3Dbrand_awareness_banner_ad%26utm_content%3Du-bin&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated MLOps Platforms Products in 2026? ### 1. [Pecan](https://www.g2.com/products/pecan/reviews) Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes. **Average Rating:** 4.7/5.0 **Total Reviews:** 36 **How Do G2 Users Rate Pecan?** - **Ease of Use:** 8.9/10 (Category avg: 8.8/10) **Who Is the Company Behind Pecan?** - **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai) - **Company Website:** https://www.pecan.ai - **Year Founded:** 2018 - **HQ Location:** US, Israel - **Twitter:** @pecan_ai (1,135 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (89 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Retail - **Company Size:** 54% Mid-Market, 21% Enterprise #### What Are Pecan's Pros and Cons? **Pros:** - Ease of Use (25 reviews) - Customer Support (18 reviews) - Speed (15 reviews) - Problem Solving (13 reviews) - Implementation Ease (11 reviews) **Cons:** - Learning Difficulty (9 reviews) - Limitations (8 reviews) - Limited Features (8 reviews) - Learning Curve (7 reviews) - Limited Customization (5 reviews) ### What Do G2 Reviewers Say About Pecan? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of Pecan, allowing simple model building without requiring deep technical skills. - Users praise Pecan's **excellent customer support** , emphasizing prompt assistance and valuable guidance throughout their learning process. - Users highlight the **speed of development** with Pecan, reducing model creation from months to weeks efficiently. - Users value Pecan's **exceptional problem-solving support** , enhancing their ability to leverage data for actionable insights. - Users highlight the **implementation ease** of Pecan, facilitating swift model deployment and enhancing productivity significantly. **Cons:** - Users experience a **steep learning curve** with Pecan, requiring at least intermediate SQL knowledge to navigate effectively. - Users desire **deeper control over model selection and custom optimization metrics** , finding the auto-selection process limiting. - Users feel the **limited model customization** restricts their ability to tailor solutions for specific use cases effectively. - Users experience a **steep learning curve** initially, particularly with data structure and SQL understanding required. - Users express a desire for **limited customization** , wishing for more control over model selection and optimization metrics. #### What Are Recent G2 Reviews of Pecan? **"[AI Chatbox Integration Makes Feature Development Easy to Explore and Iterate](https://www.g2.com/survey_responses/pecan-review-12878894)"** **Rating:** 4.0/5.0 stars *— Yuqi L.* [Read full review](https://www.g2.com/survey_responses/pecan-review-12878894) --- **"[Intuitive Platform with Exceptional Support](https://www.g2.com/survey_responses/pecan-review-12654479)"** **Rating:** 5.0/5.0 stars *— J G.* [Read full review](https://www.g2.com/survey_responses/pecan-review-12654479) --- ### 2. [Arize AI](https://www.g2.com/products/arize-ai/reviews) Arize AI offers an all-in-one AI and Agent Engineering platform designed for the complexity and unpredictable behavior of generative models. With purpose-built tools to observe, evaluate, and optimize performance, teams can detect issues early, understand why they occur, and improve reliability from development through production. Open and interoperable by design, Arize enables faster iteration, safer deployments, and more reliable customer experiences while remaining agnostic to vendor, framework, and language. Prompt IDE: Design, test, and evolve prompts with live inputs, outputs, and evaluation results Tracing & Observability: Visualize every step of an agent’s behavior with Arize’s OpenInference instrumentation Evaluation: Run online and offline LLM-as-a-Judge and human feedback loops to measure accuracy and task success Continuous Improvement: Use trace analysis, evaluation feedback, and curated datasets to run experiments and improve agents Co-pilot assistant (Alyx): Ask natural language question about agent performance within the Arize platform Real-time Monitoring & Alerts: Track custom metrics, monitor latency, token usage, failures, and set alerts to stay ahead of production issues **Average Rating:** 4.3/5.0 **Total Reviews:** 32 **How Do G2 Users Rate Arize AI?** - **Ease of Use:** 8.1/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 9.0/10) - **Metrics:** 9.2/10 (Category avg: 8.7/10) - **Framework Flexibility:** 7.5/10 (Category avg: 8.7/10) **Who Is the Company Behind Arize AI?** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,614 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (197 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 42% Small-Business, 33% Mid-Market #### What Are Arize AI's Pros and Cons? **Pros:** - Ease of Use (4 reviews) - Features (4 reviews) - Capabilities (2 reviews) - Customer Support (2 reviews) - Data Visualization (2 reviews) **Cons:** - Missing Features (3 reviews) - Performance Issues (2 reviews) - Slow Performance (2 reviews) - API Issues (1 reviews) - Difficult Learning (1 reviews) ### What Do G2 Reviewers Say About Arize AI? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **intuitive interface** of Arize AI, which simplifies monitoring and understanding machine learning models. - Users appreciate the **comprehensive model monitoring features** of Arize AI, enabling effective ML operations and quick onboarding. - Users appreciate the **real-time monitoring capabilities** of Arize AI, enhancing their understanding and management of machine learning models. - Users commend the **responsive and diligent support team** of Arize AI, enhancing their overall experience and installation process. - Users value the **smooth visualization capabilities** of Arize AI, enhancing their machine learning monitoring experience effectively. **Cons:** - Users note a lack of **missing features** in Arize AI, which limits its potential and relevance in LLM work. - Users report **performance issues** with Arize AI, experiencing slow response times and rendering challenges with large datasets. - Users report **slow performance** in Arize AI, particularly with UI response times and large dataset visualizations. - Users desire a **better API integration** in Arize AI for enhanced feature accessibility and usability. - Users find the **difficult learning curve** of Arize AI challenging, especially for newcomers to machine learning operations. #### What Are Recent G2 Reviews of Arize AI? **"[Arize AI: Clear model monitoring with strong integrations and analyses](https://www.g2.com/survey_responses/arize-ai-review-13101919)"** **Rating:** 4.5/5.0 stars *— Rafael A.* [Read full review](https://www.g2.com/survey_responses/arize-ai-review-13101919) --- **"[Enterprise-Ready AI Observability with Automated Eval Loops and Real-Time Telemetry](https://www.g2.com/survey_responses/arize-ai-review-12984903)"** **Rating:** 4.0/5.0 stars *— Corey W.* [Read full review](https://www.g2.com/survey_responses/arize-ai-review-12984903) --- ### 3. [Datature](https://www.g2.com/products/datature/reviews) Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented tools and complex infrastructure, teams can focus on solving real-world problems. **Average Rating:** 4.9/5.0 **Total Reviews:** 39 **How Do G2 Users Rate Datature?** - **Ease of Use:** 9.5/10 (Category avg: 8.8/10) - **Scalability:** 9.5/10 (Category avg: 9.0/10) - **Metrics:** 9.3/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.9/10 (Category avg: 8.7/10) **Who Is the Company Behind Datature?** - **Seller:** [Datature](https://www.g2.com/sellers/datature) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **Twitter:** @DatatureAI (168 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/datature/ (23 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Research - **Company Size:** 64% Small-Business, 28% Enterprise #### What Are Datature's Pros and Cons? **Pros:** - Efficiency (5 reviews) - Annotation Efficiency (4 reviews) - Ease of Use (4 reviews) - Model Management (4 reviews) - AI Capabilities (3 reviews) **Cons:** - Limited Customization (2 reviews) - Annotation Issues (1 reviews) - Difficult Learning (1 reviews) - Difficult Setup (1 reviews) - Expensive (1 reviews) ### What Do G2 Reviewers Say About Datature? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **efficiency** of Datature, enabling quick data labeling and streamlined model training for faster project progress. - Users value the **annotation efficiency** of Datature, streamlining their machine learning projects with user-friendly tools. - Users find Datature to be **extremely user-friendly** , simplifying data labeling and model training for all skill levels. - Users value the **extensive model types** and options in Datature, praising its support and user-friendly design. - Users appreciate the **efficient AI capabilities** of Datature, enhancing data labeling and model training processes significantly. **Cons:** - Users find **limited customization options** in Datature may hinder advanced users seeking more control over their setups. - Users experience **annotation tool clarity issues** , yet prompt support in Slack helps resolve problems quickly. - Users experience a **difficult learning curve** when setting up labeling jobs, despite finding model building easy. - Users face a **difficult setup** for labeling jobs, which may hinder their initial experience with Datature. - Users note the **high cost** of Datature, which may be a barrier for personal users despite its quality. #### What Are Recent G2 Reviews of Datature? **"[Impressive range of CV models makes custom Vision AI projects easy](https://www.g2.com/survey_responses/datature-review-12102117)"** **Rating:** 4.5/5.0 stars *— Phil B.* [Read full review](https://www.g2.com/survey_responses/datature-review-12102117) --- **"[Extremely User-Friendly Platform Backed by a Kind and Committed Team](https://www.g2.com/survey_responses/datature-review-13081746)"** **Rating:** 5.0/5.0 stars *— Sam R.* [Read full review](https://www.g2.com/survey_responses/datature-review-13081746) --- ### 4. [SAS Model Manager](https://www.g2.com/products/sas-model-manager/reviews) SAS® Model Manager is a web-based application that enables organizations to register, modify, track, score, publish, and report on analytical models. Organizations can store models within folders or projects, develop and validate candidate models, and assess candidate models for champion model selection. They can then publish and monitor champion models. All model development and model maintenance personnel, including data modelers, validation testers, scoring officers, and analysts can use SAS Model Manager. **Average Rating:** 4.6/5.0 **Total Reviews:** 56 **How Do G2 Users Rate SAS Model Manager?** - **Ease of Use:** 8.0/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 9.0/10) - **Metrics:** 7.5/10 (Category avg: 8.7/10) - **Framework Flexibility:** 7.5/10 (Category avg: 8.7/10) **Who Is the Company Behind SAS Model Manager?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,863 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®) - **Phone:** 1-800-727-0025 **Who Uses This Product?** - **Who Uses This:** Inside Sales Manager - **Top Industries:** Computer Software - **Company Size:** 59% Enterprise, 27% Small-Business #### What Are SAS Model Manager's Pros and Cons? **Pros:** - Model Management (3 reviews) - Model Variety (3 reviews) - Analytics (2 reviews) - Automation (1 reviews) - Collaboration (1 reviews) **Cons:** - Learning Curve (2 reviews) - Complexity (1 reviews) - Complexity Issues (1 reviews) - Difficult Learning (1 reviews) - Difficult Navigation (1 reviews) ### What Do G2 Reviewers Say About SAS Model Manager? *AI-generated summary from verified user reviews* **Pros:** - Users love the **collaborative capabilities** of Model Manager, allowing seamless sharing and simplification of model management. - Users appreciate the **variety of model management features** in SAS Model Manager, enhancing collaboration and simplifying ML processes. - Users value the **simplicity and efficiency** of SAS Model Manager for managing classical ML models and analytics processes. - Users appreciate the **automation of processes** in SAS Model Manager, significantly reducing time and effort for model training. - Users value the **collaboration features** in SAS Model Manager, enabling seamless sharing and teamwork on models. **Cons:** - Users find a steep **learning curve** due to challenging navigation in the documentation, complicating the usage of SAS Model Manager. - Users find the **complexity** of SAS Model Manager overwhelming, making it difficult to navigate the system effectively. - Users report **complexity issues** with SAS Model Manager, leading to challenges in usability and adoption. - Users find **difficult learning** in SAS Model Manager, especially when navigating parameter tuning options effectively. - Users struggle with **difficult navigation** in SAS Model Manager, making it hard to find specific documentation and information. #### What Are Recent G2 Reviews of SAS Model Manager? **"[Transforms Model Deployment with Ease](https://www.g2.com/survey_responses/sas-model-manager-review-12704748)"** **Rating:** 4.0/5.0 stars *— Surya Teja P.* [Read full review](https://www.g2.com/survey_responses/sas-model-manager-review-12704748) --- **"[Straightforward, Clean Interface That’s Easy to Use](https://www.g2.com/survey_responses/sas-model-manager-review-12708119)"** **Rating:** 4.0/5.0 stars *— Wen-Hung W.* [Read full review](https://www.g2.com/survey_responses/sas-model-manager-review-12708119) --- ### 5. [Labelbox](https://www.g2.com/products/labelbox/reviews) Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products with custom or foundation models, or using AI to automate data tasks or find business insights, Labelbox enables teams to do so effectively and quickly. The platform is used by Fortune 500 enterprises such as Walmart, P&G, Genentech, and Adobe, and hundreds of leading AI teams. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), and Databricks Ventures. **Average Rating:** 4.5/5.0 **Total Reviews:** 48 **How Do G2 Users Rate Labelbox?** - **Ease of Use:** 9.0/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 9.0/10) - **Metrics:** 8.9/10 (Category avg: 8.7/10) - **Framework Flexibility:** 10.0/10 (Category avg: 8.7/10) **Who Is the Company Behind Labelbox?** - **Seller:** [Labelbox](https://www.g2.com/sellers/labelbox) - **Year Founded:** 2018 - **HQ Location:** San Francisco, California - **Twitter:** @labelbox (3,489 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/labelbox/ (469 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 46% Small-Business, 38% Mid-Market #### What Are Labelbox's Pros and Cons? **Pros:** - Ease of Use (9 reviews) - Data Labeling (6 reviews) - Efficiency (6 reviews) - AI Capabilities (5 reviews) - Easy Integrations (5 reviews) **Cons:** - Lack of Features (3 reviews) - Slow Performance (3 reviews) - Difficult Learning (2 reviews) - Expensive (2 reviews) - Slow Processing (2 reviews) ### What Do G2 Reviewers Say About Labelbox? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **ease of use** of Labelbox, highlighting its simple setup and accessible features for project management. - Users commend the **easy and fast data labeling** process of Labelbox, enhancing workflow and improving data quality effectively. - Users enjoy the **efficient project management** capabilities of Labelbox, streamlining tasks and enhancing overall organization. - Users value the **AI capabilities** of Labelbox for simplifying data labeling and improving model accuracy effectively. - Users find **easy integrations** with Labelbox, enhancing their experience with a user-friendly interface and quick setup. **Cons:** - Users express frustration with the **lack of features** , feeling limited by the inability to customize and increase task availability. - Users experience **slow performance** with Labelbox, especially when working with large datasets and output visualizations. - Users find Labelbox to have **difficult learning** due to its complexity and slow processing with large datasets. - Users find Labelbox **expensive** and express concerns about the high cost, especially for small-scale users. - Users express frustration with the **slow processing** speed of Labelbox, delaying project initiation and overall experience. #### What Are Recent G2 Reviews of Labelbox? **"[LLM Training at it’s finest!](https://www.g2.com/survey_responses/labelbox-review-11265400)"** **Rating:** 4.5/5.0 stars *— Staci T.* [Read full review](https://www.g2.com/survey_responses/labelbox-review-11265400) --- **"[Professional Interface, Simple Setup, Needs Data Update](https://www.g2.com/survey_responses/labelbox-review-12625977)"** **Rating:** 4.0/5.0 stars *— Ashish S.* [Read full review](https://www.g2.com/survey_responses/labelbox-review-12625977) --- #### What Are G2 Users Discussing About Labelbox? - [How do I create a labeled dataset?](https://www.g2.com/discussions/how-do-i-create-a-labeled-dataset) - [What is label tool?](https://www.g2.com/discussions/what-is-label-tool) - [How do I download Labelbox?](https://www.g2.com/discussions/how-do-i-download-labelbox) - 2 comments - [Is Labelbox open source?](https://www.g2.com/discussions/is-labelbox-open-source) ### 6. [Valohai](https://www.g2.com/products/valohai/reviews) Valohai is the MLOps platform purpose-built for ML Pioneers, giving them everything they've been missing, in one platform that just makes sense. Now they run thousands of experiments at the click of a button – creating data they trust. All while using the tools they love to build things to last. And with Valohai, ML teams easily collaborate on anything from models to metrics. Allowing ML Pioneers to build faster and deliver stronger products to the world. Pushing the boundaries of what anyone out there ever dreamed they could do with ML. **Average Rating:** 4.9/5.0 **Total Reviews:** 26 **How Do G2 Users Rate Valohai?** - **Ease of Use:** 9.3/10 (Category avg: 8.8/10) - **Scalability:** 9.4/10 (Category avg: 9.0/10) - **Metrics:** 9.1/10 (Category avg: 8.7/10) - **Framework Flexibility:** 9.7/10 (Category avg: 8.7/10) **Who Is the Company Behind Valohai?** - **Seller:** [Valohai Ltd](https://www.g2.com/sellers/valohai-ltd) - **Year Founded:** 2016 - **HQ Location:** San Francisco, CA - **Twitter:** @valohaiai (1,829 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/15250931 (20 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Automotive - **Company Size:** 35% Small-Business, 31% Mid-Market #### What Are Valohai's Pros and Cons? **Pros:** - Capabilities (1 reviews) - Customization Flexibility (1 reviews) - Ease of Use (1 reviews) - Features (1 reviews) - Flexibility (1 reviews) **Cons:** - Error Management (1 reviews) - Lack of Tools (1 reviews) ### What Do G2 Reviewers Say About Valohai? *AI-generated summary from verified user reviews* **Pros:** - Users value the **highly flexible environment** of Valohai, enabling them to achieve their goals with ease. - Users value the **customization flexibility** of Valohai, enabling them to tailor the platform to their specific needs. - Users value the **ease of use** of Valohai, enjoying its straightforward features and flexible environment for tasks. - Users value Valohai's **flexibility and reliability** , enabling them to execute a wide range of tasks effortlessly. - Users value the **flexibility** of Valohai, enabling them to execute a wide range of tasks effortlessly. **Cons:** - Users face **session retention issues** and lack of dedicated data storage, making notebooks less ideal for use. - Users find the **lack of dedicated notebook data storage** a significant drawback, affecting usability and session retention. #### What Are Recent G2 Reviews of Valohai? **"[Indispensable tool for collaboration on ML projects](https://www.g2.com/survey_responses/valohai-review-8932310)"** **Rating:** 5.0/5.0 stars *— Claudia L. P.* [Read full review](https://www.g2.com/survey_responses/valohai-review-8932310) --- **"[Exceptionally Flexible and Reliable Platform for ML Workflows](https://www.g2.com/survey_responses/valohai-review-12076035)"** **Rating:** 4.5/5.0 stars *— Verified User in Leisure, Travel & Tourism* [Read full review](https://www.g2.com/survey_responses/valohai-review-12076035) --- #### What Are G2 Users Discussing About Valohai? - [What is Valohai used for?](https://www.g2.com/discussions/what-is-valohai-used-for) - 1 comment ### 7. [Cloudera](https://www.g2.com/products/cloudera/reviews) Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments. **Average Rating:** 4.1/5.0 **Total Reviews:** 131 **How Do G2 Users Rate Cloudera?** - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind Cloudera?** - **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera) - **Company Website:** https://www.cloudera.com - **Year Founded:** 2008 - **HQ Location:** Santa Clara, CA - **Twitter:** @cloudera (106,442 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,446 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 32% Small-Business #### What Are Cloudera's Pros and Cons? **Pros:** - Ease of Use (22 reviews) - Scalability (17 reviews) - Security (9 reviews) - Data Management (8 reviews) - Features (8 reviews) **Cons:** - Expensive (16 reviews) - Complexity (7 reviews) - Difficult Learning (5 reviews) - Poor Documentation (4 reviews) - Access Issues (3 reviews) ### What Do G2 Reviewers Say About Cloudera? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **brilliant and easy-to-use interface** of Cloudera, enhancing their data analysis experience. - Users value the **seamless scalability** of Cloudera, effectively handling vast amounts of data with ease. - Users appreciate the **strong security features** of Cloudera, ensuring reliable data management and protection. - Users value Cloudera for its **comprehensive big data management tools** , enhancing their data handling and scalability experience. - Users appreciate the **scalability and ease of use** of Cloudera, making data management and reporting effortless. **Cons:** - Users note that Cloudera's platform can be **expensive** to maintain and challenging to set up, especially for beginners. - Users find the **complexity** of Cloudera challenging, especially when managing SQL queries and customization. - Users find the **learning curve steep** with Cloudera, making setup and navigation challenging for beginners. - Users find Cloudera's **poor documentation** makes navigating complex data configurations challenging and complicates error resolution. - Users face significant **access issues** with Cloudera, including authorization errors and limited documentation support. #### What Are Recent G2 Reviews of Cloudera? **"[Reliable Platform for Managing Large-Scale Data Pipelines](https://www.g2.com/survey_responses/cloudera-review-11455117)"** **Rating:** 4.5/5.0 stars *— Paritosh C.* [Read full review](https://www.g2.com/survey_responses/cloudera-review-11455117) --- **"[Easy to Use, Reliable, and Great for Team Collaboration](https://www.g2.com/survey_responses/cloudera-review-12695378)"** **Rating:** 4.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/cloudera-review-12695378) --- #### What Are G2 Users Discussing About Cloudera? - [What is Cloudera used for?](https://www.g2.com/discussions/what-is-cloudera-used-for) - 1 comment - [What is Hortonworks Data Platform used for?](https://www.g2.com/discussions/what-is-hortonworks-data-platform-used-for) - [What is Cloudera Data Flow used for?](https://www.g2.com/discussions/what-is-cloudera-data-flow-used-for) - [What is Cloudera Navigator used for?](https://www.g2.com/discussions/what-is-cloudera-navigator-used-for) - [What is Cloudera Data Engineering used for?](https://www.g2.com/discussions/what-is-cloudera-data-engineering-used-for) ### 8. [WhyLabs](https://www.g2.com/products/whylabs/reviews) The ability to control and observe the health of AI applications is critical for the success and ROI of every experience powered by either predictive or generative AI models. WhyLabs enables teams to deploy AI applications responsibly and run them without failure. From Fortune 100 companies to AI-first startups, teams have adopted WhyLabs’ tools to monitor ML and generative AI applications. WhyLabs’ open source tools and SaaS observability platform surface drift, data quality issues, bias, and hallucinations. With WhyLabs, teams reduce manual operations by over 80% and cut down time-to-resolution of AI incidents by 20x. **Average Rating:** 4.6/5.0 **Total Reviews:** 27 **How Do G2 Users Rate WhyLabs?** - **Ease of Use:** 8.5/10 (Category avg: 8.8/10) - **Scalability:** 8.1/10 (Category avg: 9.0/10) - **Metrics:** 9.1/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.9/10 (Category avg: 8.7/10) **Who Is the Company Behind WhyLabs?** - **Seller:** [WhyLabs](https://www.g2.com/sellers/whylabs) - **Year Founded:** 2019 - **HQ Location:** Seattle, WA - **Twitter:** @WhyLabs (1,182 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/whylabsai/ (54 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 48% Small-Business, 26% Mid-Market #### What Are WhyLabs's Pros and Cons? **Pros:** - Customer Support (4 reviews) - AI Capabilities (1 reviews) - Analytics (1 reviews) - Capabilities (1 reviews) - Ease of Use (1 reviews) **Cons:** - API Issues (2 reviews) - Missing Features (2 reviews) - Poor Documentation (2 reviews) - Difficult Setup (1 reviews) - Lack of Guidance (1 reviews) ### What Do G2 Reviewers Say About WhyLabs? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **responsive and helpful customer support** from WhyLabs, enhancing their overall experience significantly. - Users appreciate the **robust data observability capabilities** of WhyLabs, enabling quick identification of critical issues. - Users value the **robust analytics** capabilities of WhyLabs for monitoring input quality and model performance effectively. - Users praise WhyLabs for its **robust data observability capabilities** , enabling quick identification of critical issues in data pipelines. - Users value the **ease of use** of WhyLabs for simple setup and effective monitoring of features. **Cons:** - Users often face **API issues** that complicate tasks and hinder a smooth experience with WhyLabs. - Users note **missing features** , such as limited options for profile management and advanced functionality still in development. - Users find the **poor documentation** of WhyLabs challenging, making setup and troubleshooting more difficult than necessary. - Users find the **difficult setup** of WhyLabs cumbersome, often encountering challenges with configuration and documentation. - Users find the **lack of guidance** challenging, particularly when building custom monitors and resolving code issues. #### What Are Recent G2 Reviews of WhyLabs? **"[A must have if you're organization is mature enough to use it](https://www.g2.com/survey_responses/whylabs-review-8390007)"** **Rating:** 4.5/5.0 stars *— Verified User in Real Estate* [Read full review](https://www.g2.com/survey_responses/whylabs-review-8390007) --- **"[Developed efficient solutions for optimizing ERP workflows through data analysis](https://www.g2.com/survey_responses/whylabs-review-10270427)"** **Rating:** 4.0/5.0 stars *— Rafael S.* [Read full review](https://www.g2.com/survey_responses/whylabs-review-10270427) --- ### 9. [Gurobi Optimizer](https://www.g2.com/products/gurobi-optimizer/reviews) With the Gurobi Optimizer, you can identify provably optimal solutions to the world’s most complex problems—including linear, nonlinear, and quadratic problems—using any combination of continuous and integer variables. Our user-friendly functionalities include multiple objectives, multiple scenarios, solution pools, general constraints, infeasibility analysis, a partition heuristic, Python matrix API, and more—all backed by our 100% PhD-level expert support. Plus, Gurobi is always free for students, faculty, researchers, and even recent graduates. Founded in 2008, Gurobi has operations in the Americas, Europe, and Asia. It serves customers across 40+ industries, including organizations like SAP, Air France, and the National Football League. Discover the Gurobi difference at gurobi.com. **Average Rating:** 4.6/5.0 **Total Reviews:** 21 **How Do G2 Users Rate Gurobi Optimizer?** - **Ease of Use:** 9.3/10 (Category avg: 8.8/10) **Who Is the Company Behind Gurobi Optimizer?** - **Seller:** [Gurobi ](https://www.g2.com/sellers/gurobi) - **Year Founded:** 2008 - **HQ Location:** Beaverton, OR - **Twitter:** @gurobi (5,050 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/511132/ (209 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 52% Enterprise, 24% Small-Business #### What Are Recent G2 Reviews of Gurobi Optimizer? **"[Easy to Use with Rich Model Support and Great Documentation](https://www.g2.com/survey_responses/gurobi-optimizer-review-12708410)"** **Rating:** 4.0/5.0 stars *— Pang L.* [Read full review](https://www.g2.com/survey_responses/gurobi-optimizer-review-12708410) --- **"[Optimization Excellence with Gurobi Optimizer](https://www.g2.com/survey_responses/gurobi-optimizer-review-8464485)"** **Rating:** 4.5/5.0 stars *— Verified User in Logistics and Supply Chain* [Read full review](https://www.g2.com/survey_responses/gurobi-optimizer-review-8464485) --- #### What Are G2 Users Discussing About Gurobi Optimizer? - [What algorithms does Gurobi use?](https://www.g2.com/discussions/what-algorithms-does-gurobi-use) - [Is Gurobi a software?](https://www.g2.com/discussions/is-gurobi-a-software) - [How much does Gurobi cost?](https://www.g2.com/discussions/how-much-does-gurobi-cost) ### 10. [Kili](https://www.g2.com/products/kili/reviews) Kili Technology is a collaborative AI data platform designed to meet the rigorous needs of building large-scale production-ready AI data securely. Founded in Paris in 2018, Kili Technology caters to a diverse range of industries, including healthcare, financial services, manufacturing, defense, and technology. The platform is engineered to support teams of varying sizes, accommodating anywhere from 1 to over 500 concurrent users, and processes millions of assets annually. The core functionality of Kili Technology lies in its ability to facilitate collaboration among cross-functional teams. Unlike traditional labeling tools that primarily serve machine learning engineers, Kili connects data science teams with business stakeholders and subject matter experts. This integration enhances the AI development lifecycle by streamlining processes from annotation and labeling to validation and model feedback. As a result, users can ensure that the data used for training AI models is not only accurate but also relevant to the specific business context. Kili Technology is particularly beneficial for organizations looking to harness the power of AI while maintaining a high level of data quality. The platform supports various data modalities, allowing teams to work with text, images, audio, and video data seamlessly. This versatility makes it suitable for a wide range of applications, from developing natural language processing models to image recognition systems. By fostering collaboration among different roles within an organization, Kili enhances the overall efficiency of the AI development process. Key features of Kili Technology include an intuitive user interface that simplifies the labeling process, robust tools for data validation, and comprehensive feedback mechanisms that enable continuous improvement of AI models. Additionally, the platform offers advanced analytics capabilities, allowing teams to track progress and identify areas for enhancement. These features collectively empower organizations to build high-quality training datasets that meet the demands of complex AI applications. Kili Technology stands out in the competitive landscape of AI data platforms by prioritizing collaboration and usability. By bridging the gap between technical and non-technical stakeholders, it ensures that the development of AI solutions is a cohesive effort. This approach not only accelerates the time to market for AI initiatives but also enhances the overall quality of the training data, ultimately leading to more effective AI models. **Average Rating:** 4.7/5.0 **Total Reviews:** 52 **How Do G2 Users Rate Kili?** - **Ease of Use:** 8.9/10 (Category avg: 8.8/10) **Who Is the Company Behind Kili?** - **Seller:** [Kili Technology](https://www.g2.com/sellers/kili-technology) - **Company Website:** https://kili-technology.com - **Year Founded:** 2018 - **HQ Location:** Paris, FR - **Twitter:** @Kili_Technology (438 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/33266852 (48 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Mid-Market, 34% Small-Business #### What Are Kili's Pros and Cons? **Pros:** - Data Labeling (1 reviews) - Data Labelling (1 reviews) - Ease of Use (1 reviews) - Model Variety (1 reviews) **Cons:** - Limited Features (1 reviews) - Missing Features (1 reviews) ### What Do G2 Reviewers Say About Kili? *AI-generated summary from verified user reviews* **Pros:** - Users love the **ease of use** of Kili's annotation platform and appreciate its comprehensive metrics for projects. - Users value the **ease of use** and precise metrics for comprehensive project visualization on Kili's annotation platform. - Users love the **ease of use** offered by Kili, enhancing their annotation experience significantly. - Users love the **variety of models** available on Kili, enhancing their annotation projects with tailored solutions. **Cons:** - Users feel that Kili lacks **adequate content updates** , limiting the platform's overall utility and engagement. - Users feel Kili lacks **adequate content updates** , limiting their overall experience and engagement with the platform. #### What Are Recent G2 Reviews of Kili? **"[Intuitive UX and Quick Installation](https://www.g2.com/survey_responses/kili-review-12342244)"** **Rating:** 5.0/5.0 stars *— Hery R.* [Read full review](https://www.g2.com/survey_responses/kili-review-12342244) --- **"[Ease of Use and Exceptional Efficiency](https://www.g2.com/survey_responses/kili-review-12354373)"** **Rating:** 5.0/5.0 stars *— Lantosoa V.* [Read full review](https://www.g2.com/survey_responses/kili-review-12354373) --- ### 11. [ClearML](https://www.g2.com/products/clearml/reviews) ClearML is an end-to-end AI platform that streamlines AI development and deployment by orchestrating workloads and optimizing infrastructure performance. The platform maximizes GPU cluster utilization and efficiency while enabling enterprises to deploy GPU-as-a-Service solutions with built-in multi-tenant architecture and flexible billing capabilities for internal cost allocation or usage-based pricing models. By vertically integrating the entire AI stack—from applications and orchestration infrastructure to underlying hardware—ClearML enhances both performance and resource utilization for generative AI and machine learning workloads. ClearML’s AI Infrastructure Platform is a three-layer solution that delivers scalable and secure GenAI/AI infrastructure at enterprise scale: Infrastructure Control Plane allows organizations to connect and manage GPU clusters – whether on-premises, in the cloud, or both – ensuring high performance and cost optimization. AI Development Center provides an environment for developing, training, and testing AI, accessible from anywhere. GenAI App Engine quickly and easily deploys AI applications and agents onto clusters with automatically configured networking, authentication, and security. **Average Rating:** 4.7/5.0 **Total Reviews:** 13 **How Do G2 Users Rate ClearML?** - **Ease of Use:** 8.9/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 8.8/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10) **Who Is the Company Behind ClearML?** - **Seller:** [ClearML](https://www.g2.com/sellers/clearml) - **Year Founded:** 2016 - **HQ Location:** Tel Aviv, IL - **Twitter:** @clearmlapp (3,763 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/clearml/ (63 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 54% Small-Business, 31% Mid-Market #### What Are Recent G2 Reviews of ClearML? **"[Observation related to ClearML](https://www.g2.com/survey_responses/clearml-review-8561811)"** **Rating:** 5.0/5.0 stars *— Md Junaid H.* [Read full review](https://www.g2.com/survey_responses/clearml-review-8561811) --- **"[Revolutionizing AI Model Management](https://www.g2.com/survey_responses/clearml-review-8531597)"** **Rating:** 5.0/5.0 stars *— Dhiren G.* [Read full review](https://www.g2.com/survey_responses/clearml-review-8531597) --- ### 12. [InRule](https://www.g2.com/products/inrule/reviews) InRule Technology® provides explainable AI Decisioning. InRule empowers its users to delight customers and improve business outcomes​ by combining automated decisioning, explainable machine learning and process automation – without code. **Average Rating:** 4.4/5.0 **Total Reviews:** 66 **How Do G2 Users Rate InRule?** - **Ease of Use:** 8.1/10 (Category avg: 8.8/10) - **Scalability:** 5.8/10 (Category avg: 9.0/10) - **Metrics:** 6.7/10 (Category avg: 8.7/10) - **Framework Flexibility:** 6.7/10 (Category avg: 8.7/10) **Who Is the Company Behind InRule?** - **Seller:** [InRule Technology, Inc](https://www.g2.com/sellers/inrule-technology-inc) - **Year Founded:** 2002 - **HQ Location:** Chicago, IL - **Twitter:** @inrule (772 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/46934/ (81 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Financial Services, Insurance - **Company Size:** 46% Mid-Market, 32% Enterprise #### What Are Recent G2 Reviews of InRule? **"[Excellent business rules engine for integration](https://www.g2.com/survey_responses/inrule-review-9657171)"** **Rating:** 5.0/5.0 stars *— Verified User in Insurance* [Read full review](https://www.g2.com/survey_responses/inrule-review-9657171) --- **"[Amazing tool to get the job done](https://www.g2.com/survey_responses/inrule-review-9655214)"** **Rating:** 4.5/5.0 stars *— Jeremiah C.* [Read full review](https://www.g2.com/survey_responses/inrule-review-9655214) --- #### What Are G2 Users Discussing About InRule? - [What is irAuthor?](https://www.g2.com/discussions/what-is-irauthor) - 1 comment - [Who owns InRule?](https://www.g2.com/discussions/who-owns-inrule) - 1 comment - [How much does InRule cost?](https://www.g2.com/discussions/how-much-does-inrule-cost) - 1 comment - [What is InRule engine?](https://www.g2.com/discussions/what-is-inrule-engine) - 1 comment ### 13. [Dataloop](https://www.g2.com/products/dataloop-dataloop/reviews) Dataloop is a cutting-edge AI Development Platform that's transforming the way organizations build AI applications. Our platform is meticulously crafted to cater to developers at the heart of the AI development process, making it simpler and more intuitive to work with data and AI models. Our comprehensive solution spans the full AI development lifecycle, offering tools and functionalities that streamline data management, annotation, model selection, and deployment. Dataloop's platform is built with a focus on collaboration, allowing developers, data scientists, and engineers to work together seamlessly, breaking down traditional silos and fostering innovation. Key features include an intuitive drag-and-drop interface for constructing data pipelines, a vast library of pre-built AI elements and models, and robust data curation and annotation capabilities. These features are designed to empower developers to rapidly prototype, iterate, and deploy AI solutions, keeping pace with the fast-evolving demands of the market. Dataloop is committed to advancing AI development by providing a developer-centric platform that addresses the complexities and challenges of AI and data management. Our vision is to democratize AI development, enabling every organization to harness the power of AI and drive forward their innovative solutions. **Average Rating:** 4.4/5.0 **Total Reviews:** 87 **How Do G2 Users Rate Dataloop?** - **Ease of Use:** 8.8/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 9.0/10) - **Metrics:** 7.8/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10) **Who Is the Company Behind Dataloop?** - **Seller:** [Dataloop](https://www.g2.com/sellers/dataloop) - **Year Founded:** 2017 - **HQ Location:** Herzliya, IL - **LinkedIn® Page:** https://www.linkedin.com/company/dataloop (52 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Mid-Market, 33% Small-Business #### What Are Dataloop's Pros and Cons? **Pros:** - Ease of Use (4 reviews) - Annotation Efficiency (2 reviews) - Annotation Tools (2 reviews) - User Interface (2 reviews) - Easy Integrations (1 reviews) **Cons:** - Complexity (1 reviews) - Confusing Syntax (1 reviews) - Difficult Navigation (1 reviews) - Lack of Communication (1 reviews) - Lack of Guidance (1 reviews) ### What Do G2 Reviewers Say About Dataloop? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Dataloop, finding the intuitive UI and annotation simple and effective. - Users appreciate the **annotation efficiency** of Dataloop, enjoying a simple interface that enhances the overall experience. - Users value the **easy annotation** capabilities of Dataloop, benefitting from its simple and intuitive interface. - Users love the **simple and easy-to-navigate interface** of Dataloop, enhancing their overall experience with the tool. - Users appreciate the **easy integrations** of Dataloop, enhancing their existing workflows effortlessly. **Cons:** - Users find the **UI changes complicated** , making the overall experience with Dataloop less intuitive. - Users find the **confusing syntax** of Dataloop frustrating, particularly after recent UI changes that impacted usability. - Users find the **difficult navigation** due to UI changes to be confusing, impacting their overall experience. - Users feel that the **lack of communication** from the community is hindering their overall experience with Dataloop. - Users feel the lack of a **demo section for first-time users** hinders their onboarding experience with Dataloop. #### What Are Recent G2 Reviews of Dataloop? **"[I have had a smooth and convenient time every day I am working on Dataloop](https://www.g2.com/survey_responses/dataloop-review-9624539)"** **Rating:** 5.0/5.0 stars *— Mzamil J.* [Read full review](https://www.g2.com/survey_responses/dataloop-review-9624539) --- **"[A journey into Data workflow with Dataloop.](https://www.g2.com/survey_responses/dataloop-review-9633025)"** **Rating:** 4.0/5.0 stars *— Dennis R.* [Read full review](https://www.g2.com/survey_responses/dataloop-review-9633025) --- #### What Are G2 Users Discussing About Dataloop? - [What are data annotations?](https://www.g2.com/discussions/dataloop-what-are-data-annotations) - 1 comment - [What are data annotations?](https://www.g2.com/discussions/what-are-data-annotations) - 1 comment - [Is Dataloop free?](https://www.g2.com/discussions/dataloop-is-dataloop-free) - 1 comment - [Is Dataloop free?](https://www.g2.com/discussions/is-dataloop-free) - 1 comment - [What are the industries that Dataloop supports?](https://www.g2.com/discussions/dataloop-what-are-the-industries-that-dataloop-supports) - 1 comment ### 14. [Kubeflow](https://www.g2.com/products/kubeflow/reviews) Kubeflow is an open-source platform designed to facilitate the deployment, orchestration, and management of machine learning (ML) workflows on Kubernetes. It provides a comprehensive suite of tools that cover the entire ML lifecycle, enabling data scientists and engineers to develop, train, and deploy models efficiently in scalable and portable environments. Key Features and Functionality: - Kubeflow Notebooks: Offers web-based development environments, such as Jupyter Notebooks, running inside Kubernetes pods, allowing for interactive model development. - Kubeflow Pipelines: Enables the creation and deployment of portable, scalable ML workflows using Kubernetes, promoting consistency and reproducibility. - Kubeflow Trainer: Supports distributed training across various AI frameworks, including PyTorch, Hugging Face, DeepSpeed, MLX, JAX, and XGBoost, facilitating large-scale model training. - Kubeflow Katib: Provides automated machine learning capabilities, including hyperparameter tuning, early stopping, and neural architecture search, to optimize model performance. - Kubeflow KServe: Delivers a standardized platform for serving ML models across multiple frameworks, ensuring scalable and efficient model inference. - Kubeflow Model Registry: Acts as a centralized repository for managing ML models, versions, and associated metadata, bridging the gap between model experimentation and production deployment. Primary Value and Problem Solved: Kubeflow addresses the complexities associated with deploying and managing ML workflows by leveraging Kubernetes' scalability and portability. It abstracts the intricacies of containerization, allowing users to focus on building, training, and deploying models without worrying about the underlying infrastructure. By automating various stages of the ML lifecycle, Kubeflow enhances reproducibility, efficiency, and collaboration among data scientists and engineers, ultimately accelerating the development and deployment of machine learning solutions. **Average Rating:** 4.5/5.0 **Total Reviews:** 21 **How Do G2 Users Rate Kubeflow?** - **Ease of Use:** 7.6/10 (Category avg: 8.8/10) **Who Is the Company Behind Kubeflow?** - **Seller:** [Kubeflow](https://www.g2.com/sellers/kubeflow) - **Year Founded:** 2017 - **HQ Location:** Sunnyvale, US - **Twitter:** @kubeflow (6,580 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kubeflow/ (34 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 48% Small-Business, 43% Enterprise #### What Are Kubeflow's Pros and Cons? **Pros:** - Efficiency (1 reviews) - Flexibility (1 reviews) - Model Variety (1 reviews) - Problem Solving (1 reviews) - Scalability (1 reviews) **Cons:** - Complexity (1 reviews) - Complex Setup (1 reviews) - Difficult Setup (1 reviews) - Limited Capacity (1 reviews) - Limited Resources (1 reviews) ### What Do G2 Reviewers Say About Kubeflow? *AI-generated summary from verified user reviews* **Pros:** - Users find that **Kubeflow makes CRON based ETL workflows quick and efficient** , enhancing their overall productivity. - Users value the **flexibility** of Kubeflow, enabling scalable and reproducible management of machine learning workflows. - Users praise the **model variety** in Kubeflow, enhancing scalability and flexibility for machine learning workloads. - Users find that Kubeflow enables **efficient problem solving** for small CRON based ETL workflows, enhancing speed and performance. - Users value the **scalability** of Kubeflow, empowering them to manage machine learning workloads efficiently and flexibly. **Cons:** - Users find the **complexity of initial setup and management** a significant challenge, requiring extensive Kubernetes knowledge. - Users find the **initial setup complex** , requiring extensive Kubernetes expertise and resources for effective management. - Users find the **difficult setup** of Kubeflow to be complex and demanding significant Kubernetes expertise. - Users find that **limited capacity** of Kubeflow hampers the feasibility of memory-intensive operations in their projects. - Users find the **limited resources** for setup and ongoing management of Kubeflow challenging, requiring significant Kubernetes expertise. #### What Are Recent G2 Reviews of Kubeflow? **"[Kuberflow Review](https://www.g2.com/survey_responses/kubeflow-review-9940747)"** **Rating:** 4.0/5.0 stars *— Barkath U.* [Read full review](https://www.g2.com/survey_responses/kubeflow-review-9940747) --- **"[Kubeflow makes it easier to run quick batch process on kubernetes platform](https://www.g2.com/survey_responses/kubeflow-review-11473381)"** **Rating:** 5.0/5.0 stars *— Aditya K.* [Read full review](https://www.g2.com/survey_responses/kubeflow-review-11473381) --- #### What Are G2 Users Discussing About Kubeflow? - [Is Kubeflow any good?](https://www.g2.com/discussions/is-kubeflow-any-good) - [What is difference between Kubernetes and Kubeflow?](https://www.g2.com/discussions/what-is-difference-between-kubernetes-and-kubeflow) - [What are the components of Kubeflow?](https://www.g2.com/discussions/what-are-the-components-of-kubeflow) - [What can Kubeflow do?](https://www.g2.com/discussions/what-can-kubeflow-do) ### 15. [Comet.ml](https://www.g2.com/products/comet-ml/reviews) Comet provides an end-to-end model evaluation platform for AI developers, with best in class LLM evaluations, experiment tracking, and production monitoring. **Average Rating:** 4.3/5.0 **Total Reviews:** 13 **How Do G2 Users Rate Comet.ml?** - **Ease of Use:** 8.1/10 (Category avg: 8.8/10) - **Scalability:** 7.3/10 (Category avg: 9.0/10) - **Metrics:** 8.0/10 (Category avg: 8.7/10) - **Framework Flexibility:** 7.7/10 (Category avg: 8.7/10) **Who Is the Company Behind Comet.ml?** - **Seller:** [Comet.ml](https://www.g2.com/sellers/comet-ml) - **Year Founded:** 2017 - **HQ Location:** New York, NY - **Twitter:** @Cometml (15,042 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/comet-ml/?viewAsMember=true (101 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 54% Mid-Market, 38% Small-Business #### What Are Recent G2 Reviews of Comet.ml? **"[Comet.ml: Streamlining Machine Learning and Collaborative Experiment Tracking Platform](https://www.g2.com/survey_responses/comet-ml-review-7692129)"** **Rating:** 5.0/5.0 stars *— Shreyansh J.* [Read full review](https://www.g2.com/survey_responses/comet-ml-review-7692129) --- **"[Fascinating AI Agent Visualization That Brings Clarity to Debugging](https://www.g2.com/survey_responses/comet-ml-review-12841137)"** **Rating:** 5.0/5.0 stars *— Verified User in Semiconductors* [Read full review](https://www.g2.com/survey_responses/comet-ml-review-12841137) --- #### What Are G2 Users Discussing About Comet.ml? - [What is ML model?](https://www.g2.com/discussions/what-is-ml-model) - [Is Comet ml open source?](https://www.g2.com/discussions/is-comet-ml-open-source) - [What is Comet machine learning?](https://www.g2.com/discussions/what-is-comet-machine-learning) - [How does Comet ML work?](https://www.g2.com/discussions/how-does-comet-ml-work) ### 16. [MLJAR](https://www.g2.com/products/mljar/reviews) Leader in creating Data Science Tools. MLJAR is an automated machine learning (AutoML) framework designed to make building and deploying machine learning models easier and more accessible. It offers tools to help users—whether they are data scientists, analysts, or non-technical individuals—create machine learning models without needing extensive programming skills, build data-powered apps, and analyze data. ! NEW ! Boost your machine learning power with MLJAR STUDIO - an innovative Python machine learning editor. MLJAR maintains open-source libraries such as: AutoML mljar-supervised Mercury Supertree... **Average Rating:** 4.4/5.0 **Total Reviews:** 16 **How Do G2 Users Rate MLJAR?** - **Ease of Use:** 8.8/10 (Category avg: 8.8/10) - **Scalability:** 8.9/10 (Category avg: 9.0/10) - **Metrics:** 9.0/10 (Category avg: 8.7/10) - **Framework Flexibility:** 9.1/10 (Category avg: 8.7/10) **Who Is the Company Behind MLJAR?** - **Seller:** [MLJAR](https://www.g2.com/sellers/mljar) - **Year Founded:** 2016 - **HQ Location:** Łapy, PL - **Twitter:** @MLJARofficial (1,461 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/17936019 (5 employees on LinkedIn®) - **Ownership:** Private **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 63% Enterprise, 25% Small-Business #### What Are Recent G2 Reviews of MLJAR? **"[User experience and views on MLJAR](https://www.g2.com/survey_responses/mljar-review-6896702)"** **Rating:** 4.0/5.0 stars *— Prince N.* [Read full review](https://www.g2.com/survey_responses/mljar-review-6896702) --- **"[MLJAR REVIEW](https://www.g2.com/survey_responses/mljar-review-6907840)"** **Rating:** 4.5/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/mljar-review-6907840) --- #### What Are G2 Users Discussing About MLJAR? - [What is MLJAR used for?](https://www.g2.com/discussions/what-is-mljar-used-for) ### 17. [Mona](https://www.g2.com/products/mona/reviews) Mona is an intelligent monitoring platform for AI in production. Data science, machine learning, and data operation teams leverage Mona to increase trust in their AI system by giving them a powerful analytical engine that can detect issues (e.g. drifts, outliers, data integrity issues & other anomalies) weeks or longer before they come to the surface. Achieve better business results by avoiding AI catastrophes and focusing your team on the specific segments where ML models are underperforming. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. Using Mona's AI fairness feature, gain transparency into your ML models and automatically surface any hidden biases. Mona has the capability to generate complete fairness reports with full user configuration, used for both internal and external audit needs. Enterprises in a variety of industries leverage Mona to monitor NLP/NLU, speech, computer vision, and machine learning use cases. Founded in 2018 by experienced product leaders and operators from Google and McKinsey & Co., the company is backed by top VCs, with offices in the US and in Israel. Mona was recognized by Gartner in the 2021 ‘Cool Vendors in Enterprise AI Operationalization and Engineering’ report. Request a demo on our website at: https://www.monalabs.io/request-demo **Average Rating:** 4.5/5.0 **Total Reviews:** 10 **How Do G2 Users Rate Mona?** - **Ease of Use:** 8.1/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 8.3/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10) **Who Is the Company Behind Mona?** - **Seller:** [Mona](https://www.g2.com/sellers/mona) - **Year Founded:** 2018 - **HQ Location:** N/A - **Twitter:** @mona_labs (41 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/mona-labs/ (15 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 40% Mid-Market #### What Are Recent G2 Reviews of Mona? **"[Sophisticated, reliable monitoring solution](https://www.g2.com/survey_responses/mona-review-7031127)"** **Rating:** 4.0/5.0 stars *— Giacomo V.* [Read full review](https://www.g2.com/survey_responses/mona-review-7031127) --- **"[Having a Nice Time Monitoring Models](https://www.g2.com/survey_responses/mona-review-6686149)"** **Rating:** 4.0/5.0 stars *— Thiago R.* [Read full review](https://www.g2.com/survey_responses/mona-review-6686149) --- #### What Are G2 Users Discussing About Mona? - [What is Mona used for?](https://www.g2.com/discussions/what-is-mona-used-for) ### 18. [DVC](https://www.g2.com/products/dvc/reviews) DVC is an open-source, Git-based data science tool. Apply version control to machine learning development, make your repo the backbone of your project, and instill best practices across your team. Find more info at: https://dvc.org DVC has introduced a new open source tool for AI Data Analytics and Actionable GenAI Insights, that can curate vast amounts of unstructured data. Find out more at https://github.com/iterative/datachain or https:/datachain.ai DVC Studio is our SaaS tool that provides seamless data and model management, experiment tracking, visualization, and automation, with Git and DVC by as your single source of truth. Find more info at: https://studio.iterative.ai We have an ever-growing community of practitioners! Join the community in our Discord server at the link below to get your questions answered or take our free online course: https://learn.iterative.ai **Average Rating:** 4.7/5.0 **Total Reviews:** 11 **How Do G2 Users Rate DVC?** - **Ease of Use:** 6.9/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (Category avg: 9.0/10) - **Metrics:** 8.1/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10) **Who Is the Company Behind DVC?** - **Seller:** [Iterative](https://www.g2.com/sellers/iterative) - **Year Founded:** 2018 - **HQ Location:** San Francisco, California - **Twitter:** @Iterativeai (533 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/iterative-ai/ (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 55% Mid-Market, 27% Small-Business #### What Are Recent G2 Reviews of DVC? **"[Great support from DVC team; flexible and very helpful tool](https://www.g2.com/survey_responses/dvc-review-8604892)"** **Rating:** 4.5/5.0 stars *— Marcelle S.* [Read full review](https://www.g2.com/survey_responses/dvc-review-8604892) --- **"[If you like the unix and open source philosophy, then with dvc you will feel home](https://www.g2.com/survey_responses/dvc-review-8439882)"** **Rating:** 5.0/5.0 stars *— Francesco C.* [Read full review](https://www.g2.com/survey_responses/dvc-review-8439882) --- #### What Are G2 Users Discussing About DVC? - [What is DVC used for?](https://www.g2.com/discussions/what-is-dvc-used-for) ### 19. [Pachyderm](https://www.g2.com/products/pachyderm/reviews) Pachyderm is cost-effective at scale, enabling data engineering teams to automate complex pipelines with sophisticated data transformations across any type of data. Our unique approach provides parallelized processing of multi-stage, language-agnostic pipelines with data versioning and data lineage tracking. Pachyderm delivers the ultimate CI/CD engine for data. **Average Rating:** 4.4/5.0 **Total Reviews:** 14 **How Do G2 Users Rate Pachyderm?** - **Ease of Use:** 7.3/10 (Category avg: 8.8/10) - **Scalability:** 8.0/10 (Category avg: 9.0/10) - **Metrics:** 8.1/10 (Category avg: 8.7/10) - **Framework Flexibility:** 7.6/10 (Category avg: 8.7/10) **Who Is the Company Behind Pachyderm?** - **Seller:** [Pachyderm](https://www.g2.com/sellers/pachyderm) - **Year Founded:** 2014 - **HQ Location:** San Francisco, California - **Twitter:** @pachyderminc (2,371 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/pachyderm-inc- (7 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 50% Mid-Market, 36% Enterprise #### What Are Recent G2 Reviews of Pachyderm? **"[Pachyderm: The Open-Source Platform for Big Data Processing and Machine Learning](https://www.g2.com/survey_responses/pachyderm-review-7709331)"** **Rating:** 5.0/5.0 stars *— Faizan M.* [Read full review](https://www.g2.com/survey_responses/pachyderm-review-7709331) --- **"[Pachyderm eases MLOps for model lineage and version control](https://www.g2.com/survey_responses/pachyderm-review-7261676)"** **Rating:** 4.5/5.0 stars *— Soumo C.* [Read full review](https://www.g2.com/survey_responses/pachyderm-review-7261676) --- #### What Are G2 Users Discussing About Pachyderm? - [What is pachyderm data?](https://www.g2.com/discussions/what-is-pachyderm-data) - [What is pachyderm machine learning?](https://www.g2.com/discussions/what-is-pachyderm-machine-learning) - [What is pachyderm tool?](https://www.g2.com/discussions/what-is-pachyderm-tool) - 1 comment ### 20. [Seldon](https://www.g2.com/products/seldon/reviews) Seldon gets machine learning models to production faster, in the most reliable way. Front-end deployment of models, explainers and canaries means users can deploy ML models and testing can be done in live environments. Metrics and dashboards can monitor models to improve performance and rapidly communicate errors for easy debugging. Model explainers mean you can understand and adjust what features are influencing the model and anomaly detection can flag drifts in data. **Average Rating:** 4.3/5.0 **Total Reviews:** 11 **How Do G2 Users Rate Seldon?** - **Ease of Use:** 7.9/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 9.0/10) - **Metrics:** 9.2/10 (Category avg: 8.7/10) - **Framework Flexibility:** 9.2/10 (Category avg: 8.7/10) **Who Is the Company Behind Seldon?** - **Seller:** [Seldon](https://www.g2.com/sellers/seldon) - **Year Founded:** 2014 - **HQ Location:** London, GB - **Twitter:** @seldon_io (2,250 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/seldon/about (91 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 45% Enterprise, 36% Small-Business #### What Are Recent G2 Reviews of Seldon? **"[Model deployment with Seldon](https://www.g2.com/survey_responses/seldon-review-7284504)"** **Rating:** 4.5/5.0 stars *— Verified User in Defense & Space* [Read full review](https://www.g2.com/survey_responses/seldon-review-7284504) --- **"[opting Seldon is good?](https://www.g2.com/survey_responses/seldon-review-6967331)"** **Rating:** 4.0/5.0 stars *— Baratam V.* [Read full review](https://www.g2.com/survey_responses/seldon-review-6967331) --- #### What Are G2 Users Discussing About Seldon? - [What is Seldon used for?](https://www.g2.com/discussions/what-is-seldon-used-for) ### 21. [Determined AI](https://www.g2.com/products/determined-ai/reviews) Determined AI takes a pragmatic, results-driven approach to deep learning, with a goal of dramatically improving the productivity of deep learning developers. Its integrated AutoML platform simplifies the entire deep learning workflow from data management to model training and deployment. **Average Rating:** 4.5/5.0 **Total Reviews:** 11 **How Do G2 Users Rate Determined AI?** - **Ease of Use:** 7.7/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 9.4/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10) **Who Is the Company Behind Determined AI?** - **Seller:** [Determined AI](https://www.g2.com/sellers/determined-ai) - **Year Founded:** 2017 - **HQ Location:** Houston, Texas - **Twitter:** @DeterminedAI (1,705 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/determined-ai/ (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 55% Small-Business, 36% Mid-Market #### What Are Recent G2 Reviews of Determined AI? **"[To determine better patterns in the segregation of data for improved analytics.](https://www.g2.com/survey_responses/determined-ai-review-5442990)"** **Rating:** 5.0/5.0 stars *— Krishna Kumar B.* [Read full review](https://www.g2.com/survey_responses/determined-ai-review-5442990) --- **"[Allows you to scale on model training](https://www.g2.com/survey_responses/determined-ai-review-6736964)"** **Rating:** 4.0/5.0 stars *— Bahjat A.* [Read full review](https://www.g2.com/survey_responses/determined-ai-review-6736964) --- #### What Are G2 Users Discussing About Determined AI? - [What is Determined AI used for?](https://www.g2.com/discussions/what-is-determined-ai-used-for) ### 22. [DagsHub](https://www.g2.com/products/dagshub/reviews) DagsHub is a platform that allows you to easily create high-quality datasets for better model performance A single AI platform to curate vision, audio, and document data - automate labeling workflows, and evaluate models. Enterprises with sensitive data, can run on their own infrastructure on-prem and get a full AI platform. Data curation - create the very best datasets. Data annotation - annotate your vision, audio, and document data. Auto labeling - automate your annotation flow with pre-built templates and active learning. Data versioning - version your datasets for reproducibility. Experiment tracking - track your experiment progress, understand trends, and compare results. Model registry - manage your models and deployments in one place. The top data scientists build AI with DagsHub including teams at: Google, Harvard Medicine, Beewise, Macso, and Mana.bio **Average Rating:** 4.8/5.0 **Total Reviews:** 14 **How Do G2 Users Rate DagsHub?** - **Ease of Use:** 9.2/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 9.0/10) - **Metrics:** 9.3/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10) **Who Is the Company Behind DagsHub?** - **Seller:** [DagsHub](https://www.g2.com/sellers/dagshub) - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/dagshub (12 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 50% Small-Business, 43% Mid-Market #### What Are DagsHub's Pros and Cons? **Pros:** - Data Management (12 reviews) - Model Management (12 reviews) - Collaboration (11 reviews) - Features (10 reviews) - Integrated Platform (10 reviews) **Cons:** - Limited Functionality (2 reviews) - Error Handling (1 reviews) - Expensive (1 reviews) - Limited Customization (1 reviews) - Limited Free Access (1 reviews) ### What Do G2 Reviewers Say About DagsHub? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **efficient data management** capabilities of DagsHub, enabling seamless organization and tracking of ML projects. - Users value the **integrated platform** of DagsHub for efficiently managing data, code, and experiments together. - Users value the **seamless collaboration** offered by DagsHub, enhancing teamwork and productivity in data management and experiments. - Users value DagsHub for its **seamless integration of data, experiments, and models** , enhancing reproducibility and collaboration in ML projects. - Users value the **integrated platform** of DagsHub for efficiently managing data, experiments, and models seamlessly. **Cons:** - Users find **limited functionality** in DagsHub, particularly regarding team size restrictions and project integration options. - Users often face **error handling issues** when pushing or loading projects on DagsHub, leading to frustration. - Users find DagsHub **expensive** due to limitations on the free plan and non-automated access for academia. - Users find **limited customization options** on the free plan restrictive, impacting team collaboration potential. - Users find the **strict limitations of the free plan** frustrating, especially with team size capped at two. #### What Are Recent G2 Reviews of DagsHub? **"[Simplifies LLM Dataset Versioning and Experiment Tracking](https://www.g2.com/survey_responses/dagshub-review-11144209)"** **Rating:** 5.0/5.0 stars *— Gourav B.* [Read full review](https://www.g2.com/survey_responses/dagshub-review-11144209) --- **"[Reliable Infrastructure for LLM Data and Model Iteration](https://www.g2.com/survey_responses/dagshub-review-11087413)"** **Rating:** 5.0/5.0 stars *— Ignacio P.* [Read full review](https://www.g2.com/survey_responses/dagshub-review-11087413) --- ### 23. [Plutoshift](https://www.g2.com/products/plutoshift-plutoshift/reviews) Plutoshift is pioneering a new category of data platform, purpose built for enterprises that provide physical products and services. The Plutoshift Operational Data Platform (ODP) applies AI and automated machine learning to unify and align the myriad of data sources into a shared system of record, enabling a new caliber of “always-on” performance monitoring and predictive analysis. For the first time, front line and remote workers can utilize current and trusted data all the way out to the operational front lines of the business, dramatically improving the speed and reliability of critical decision making. **Average Rating:** 4.8/5.0 **Total Reviews:** 20 **How Do G2 Users Rate Plutoshift?** - **Ease of Use:** 9.5/10 (Category avg: 8.8/10) - **Scalability:** 9.2/10 (Category avg: 9.0/10) - **Metrics:** 9.2/10 (Category avg: 8.7/10) - **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10) **Who Is the Company Behind Plutoshift?** - **Seller:** [Plutoshift](https://www.g2.com/sellers/plutoshift) - **Year Founded:** 2024 - **HQ Location:** Palo Alto, California - **Twitter:** @plutoshift (229 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/plutoshift-ai/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Computer Software - **Company Size:** 65% Mid-Market, 25% Enterprise #### What Are Plutoshift's Pros and Cons? **Pros:** - Data Analysis (10 reviews) - Features (8 reviews) - Support Efficiency (8 reviews) - Analysis Capabilities (6 reviews) - Analytics (6 reviews) **Cons:** - Complex Usability (6 reviews) - Complexity (3 reviews) - Integration Issues (3 reviews) - Learning Curve (3 reviews) - Difficult Setup (2 reviews) ### What Do G2 Reviewers Say About Plutoshift? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of data analysis** with Plutoshift, enhancing decision-making through automated insights and real-time intelligence. - Users value Plutoshift for its ability to provide **real-time actionable insights** that simplify complex data and enhance decision-making. - Users value the **responsive support team** of Plutoshift, enhancing their experience with efficient assistance and solutions. - Users value the **real-time analysis capabilities** of Plutoshift, which simplify complex data for informed decision-making. - Users appreciate the **real-time actionable insights** provided by Plutoshift's innovative analytics technology, enhancing decision-making processes. **Cons:** - Users find Plutoshift's **complex usability** challenging, particularly for those less familiar with technology and setup processes. - Users find Plutoshift's **complexity** challenging, particularly when connecting data sources and configuring settings. - Users find **integration issues** frustrating, as connecting data sources and configuring settings can be time-consuming and challenging. - Users find Plutoshift's **learning curve challenging** , particularly for those lacking technical expertise or familiarity. - Users find the **difficult setup** of Plutoshift time-consuming, especially for those lacking technical skills. #### What Are Recent G2 Reviews of Plutoshift? **"[Simplifies Resource Management with Easy Setup](https://www.g2.com/survey_responses/plutoshift-review-12393684)"** **Rating:** 4.0/5.0 stars *— Jeni J.* [Read full review](https://www.g2.com/survey_responses/plutoshift-review-12393684) --- **"[Committed to Managing Sites Operations.](https://www.g2.com/survey_responses/plutoshift-review-10796025)"** **Rating:** 4.5/5.0 stars *— Davide P.* [Read full review](https://www.g2.com/survey_responses/plutoshift-review-10796025) --- ### 24. [Prism](https://www.g2.com/products/pixis-prism/reviews) Pixis is the digital marketer’s AI sidekick, that empowers you to use your own data across platforms to reach the best outcomes with confidence, all while saving teams time and effort. Our AI-powered platform brings out the hero in marketers, supercharging their decisions and empowering them to turn data into automated action. It helps marketers streamline ad manager bid and budget allocation, audience targeting, and creative asset management using tailor-made, true AI for their brand (not rules-based). This allows marketing teams to leverage real-time data to provide the best optimizations possible. These actions can be done manually, or fully automated by the marketer. This leads to meaningful, actionable insights that can be used to create better marketing decisions, whether it's dynamically optimizing ad budgets, bids, or targeting new cohorts and audiences. Pixis can also help brands build creative assets quickly, and iterate on them intelligently based on confidence scores indicating what works best with which audiences. Marketing teams can choose to implement brand, compliance, and security guidelines that Pixis will follow as it builds out creative assets. With Pixis, brands can grow and profitably scale with ease. Marketers can set-and-forget (automate) many of their tasks and receive easy-to understand recommendations on how to optimize their ad spend, reduce costs, save time, build compelling creative assets, and reach new untapped audiences. Pixis is a service that allows marketers to benefit from true AI, with minimal direction. Just tell Pixis what your objectives are, and we’ll do the rest! We currently serve over 200+ customers which include Fortune 500 brands like DHL and Sanofi, along with large enterprises like HelloFresh and Allbirds. Marketing heroes need a sidekick that frees them from toil, manual analysis, and guesswork. Pixis can empower marketers to focus on what matters, and can empower agencies to focus on high-level strategy, or bringing in new business. Ask for a demo to see how Pixis can revolutionize your ad campaigns! **Average Rating:** 4.6/5.0 **Total Reviews:** 14 **How Do G2 Users Rate Prism?** - **Ease of Use:** 9.6/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 9.6/10 (Category avg: 8.7/10) - **Framework Flexibility:** 9.2/10 (Category avg: 8.7/10) **Who Is the Company Behind Prism?** - **Seller:** [Pixis](https://www.g2.com/sellers/pixis) - **Year Founded:** 2018 - **HQ Location:** California , US - **Twitter:** @Pixis_AI (154 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/31559415 (434 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 43% Enterprise #### What Are Recent G2 Reviews of Prism? **"[Pixis Helped Us Scale New Users for Swiggy Food Delivery App at a Much Better Cost per Acquisition](https://www.g2.com/survey_responses/prism-review-8041973)"** **Rating:** 4.5/5.0 stars *— Awant B.* [Read full review](https://www.g2.com/survey_responses/prism-review-8041973) --- **"[With Pixis AI, we were able to scale high-interest audiences and increase in the Conversion Rate](https://www.g2.com/survey_responses/prism-review-8037229)"** **Rating:** 5.0/5.0 stars *— Akash S.* [Read full review](https://www.g2.com/survey_responses/prism-review-8037229) --- ### 25. [SAP Business Data Cloud](https://www.g2.com/products/sap-business-data-cloud/reviews) SAP Business Data Cloud is a fully managed software-as-a-service (SaaS) solution that unifies and governs SAP data and connects with third-party data. As an evolution of the company's data, planning, and analytics solutions, SAP Business Data Cloud brings together SAP Datasphere, SAP Analytics Cloud, and SAP Business Warehouse with a unified experience that delivers insights across all lines of business. In addition, SAP Databricks is natively available in Business Data Cloud - bringing the power of Databricks Data Intelligence Platform capabilities to the product. SAP Business Data Cloud connects data by leveraging business data fabric principles, making it easier to discover, share, govern, and model this data. It includes SAP Databricks as a first-party data service. The platform combines prebuilt applications and data products across all lines of business. It provides fully managed, curated data products across all lines of business and eliminate the costs of data extracts. Users can build on SAP’s curated data products with their domain expertise, and deliver Intelligent Applications through the Business Data Cloud ecosystem. These intelligent applications are adaptive, AI-powered applications that learn from your data, understand business context, and act on your behalf to transform business outcomes. **Average Rating:** 4.2/5.0 **Total Reviews:** 74 **How Do G2 Users Rate SAP Business Data Cloud?** - **Ease of Use:** 8.1/10 (Category avg: 8.8/10) **Who Is the Company Behind SAP Business Data Cloud?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Company Website:** https://www.sap.com/ - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,052 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Enterprise, 29% Small-Business #### What Are SAP Business Data Cloud's Pros and Cons? **Pros:** - Ease of Use (32 reviews) - Features (32 reviews) - Integration Capabilities (31 reviews) - Data Discovery (30 reviews) - Integrations (27 reviews) **Cons:** - Complexity (30 reviews) - Difficult Learning (25 reviews) - Integration Issues (25 reviews) - Expensive (23 reviews) - Learning Curve (18 reviews) ### What Do G2 Reviewers Say About SAP Business Data Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of SAP Business Data Cloud, benefiting from streamlined access to trusted data. - Users value the **seamless data integration** and robust governance of SAP Business Data Cloud, enhancing organizational efficiency. - Users value the **integration capabilities** of SAP Business Data Cloud, effectively unifying data from diverse sources for enhanced efficiency. - Users value the **single, trusted view of data** offered by SAP Business Data Cloud, enhancing efficiency and organization. - Users value the **seamless integration** capabilities of SAP Business Data Cloud, enhancing data management and decision-making efficiency. **Cons:** - Users find the **setup complexity** of SAP Business Data Cloud daunting, especially for hybrid environments and data integration. - Users face a **difficult learning curve** due to the complexity and advanced features of SAP Business Data Cloud. - Users face **integration issues** with SAP Business Data Cloud, making setup and configuration challenging in hybrid environments. - Users highlight the **high costs** associated with SAP Business Data Cloud for large-scale implementations, affecting budget considerations. - Users note a **steep learning curve** , making it challenging for new users to navigate SAP Business Data Cloud effectively. #### What Are Recent G2 Reviews of SAP Business Data Cloud? **"[Unify SAP and non-SAP data](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12851557)"** **Rating:** 4.0/5.0 stars *— Maria Francesca I.* [Read full review](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12851557) --- **"[Centralized Data Management with User-Friendly UI](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12609585)"** **Rating:** 4.0/5.0 stars *— Tejas Kumar V.* [Read full review](https://www.g2.com/survey_responses/sap-business-data-cloud-review-12609585) --- ## What Is MLOps Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to MLOps Platforms? - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Machine Learning Software](https://www.g2.com/categories/machine-learning) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [Data Labeling Software](https://www.g2.com/categories/data-labeling) - [Generative AI Infrastructure Software](https://www.g2.com/categories/generative-ai-infrastructure) - [Large Language Model Operationalization (LLMOps) Software](https://www.g2.com/categories/large-language-model-operationalization-llmops) - [ Low-Code Machine Learning Platforms Software](https://www.g2.com/categories/low-code-machine-learning-platforms) --- ## How Do You Choose the Right MLOps Platforms? ### What You Should Know About MLOps Platforms ### What are MLOps Platforms? MLOps solutions apply tools and resources to ensure that machine learning projects are run properly and efficiently, including data governance, model management, and model deployment. The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With machine learning, users are enabled to mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and helps make data-driven predictions. One crucial aspect of the machine learning process is the development, management, and monitoring of machine learning models. Users leverage MLOps Platforms to manage and monitor machine learning models as they are integrated into business applications.  Although MLOps capabilities can come together in software products or platforms, it is fundamentally a methodology. When data scientists, data engineers, developers, and other business stakeholders collaborate and ensure that the data is properly managed and mined for meaning, they need MLOps to ensure that teams are aligned, and that machine learning projects are tracked and can be reproduced. #### What Types of MLOps Platforms Exist? Not all MLOps Platforms are created equal. These tools allow developers and data scientists to manage and monitor machine learning models. However, they differ in terms of the data types supported, as well as the method and manner of deployment.  **Cloud** With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insights from it as well as to ensure its quality. These platforms allow them to train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models which have been deployed. **On-premises** Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for a number of reasons, including data security and latency issues. In cases like health care, strict regulations such as HIPAA require data to be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes vital. **Edge** Some platforms allow for spinning up algorithms on the edge, which consists of a mesh network of data centers that process and store data locally prior to being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** ** ### What are the Common Features of MLOps Platforms? The following are some core features within MLOps Platforms that can be useful to users: **Model training:** Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and results in improved model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process whereby the proper values are determined for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data. **Model management:** The process does not end once the model is released. Businesses must monitor and manage their models to ensure they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. It can help with recording, cataloging, and organizing all machine learning models deployed across the business. Not all models are meant for all users. Therefore, some tools allow for provisioning users based on authorization to both deploy and iterate upon machine learning models. **Model deployment:** The deployment of machine learning models is the process of making the models available in production environments, where they provide predictions to other software systems. Some tools allow users to manage model artifacts and track which models are deployed in production. Methods of deployments take the form of REST APIs, GUI for on-demand analysis, and more. **Metrics:** Users can control model usage and performance in production. This helps track how the models are performing. ### What are the Benefits of MLOps Platforms? Through the use of MLOps Platforms, data scientists can gain visibility into their machine learning endeavors. This helps them better understand what is and isn’t working, and they are provided with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production. **Share data insights:** Users are enabled to share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork. **Simplify and scale data science:** Pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms efficiently help scale experiments across many nodes to perform distributed training on large datasets. **Experiment better:** Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. MLOps Platforms facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning are also used in experimentation, which are algorithms or methods used to change the attributes of neural networks such as weights and learning rate to reduce the losses. ### Who Uses MLOps Platforms? Data scientists are in high demand, but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more); therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms. In addition, for data science projects to initiate, it is key that the broader business buys into these projects. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted. **Data engineers:** With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization. **Citizen data scientists:** Especially with the rise of more user-friendly features, citizen data scientists who are not professionally trained but have developed data skills are increasingly turning to MLOps to bring AI into their organization. **Professional data scientists:** Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, speeding up data exploration and preparation, as well as model development and training. **Business stakeholders:** Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations. ### What are the Alternatives to MLOps Platforms? Alternatives to MLOps Platforms can replace this type of software, either partially or completely: [Data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **:** Depending on the use case, businesses might consider data science and machine learning platforms. This software provides a platform for the full end-to-end development of machine learning models and can provide more robust features around operationalizing these algorithms. [Machine learning software](https://www.g2.com/categories/machine-learning) **:** MLOps Platforms are great for the full-scale monitoring and managing of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs. Many different types of machine learning algorithms perform various tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions. #### Software Related to MLOps Platforms Related solutions that can be used together with MLOps Platforms include: [Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although MLOps Platforms offer data preparation features, businesses might opt for a dedicated preparation tool. [Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications, allowing business intelligence and analytics tools to pull all company data from a single repository.  [Data labeling software](https://www.g2.com/categories/data-labeling) **:** To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms. [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** NLP allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide voice recognition and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions. ### Challenges with MLOps Platforms Software solutions can come with their own set of challenges.  **Data requirements:** For most AI algorithms, a great deal of data is required to make it learn the needful. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application. **Skill shortage:** There is also a shortage of people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems. **Algorithmic bias:** Although the technology is efficient, it is not always effective and is marred with various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems. ### Which Companies Should Buy MLOps Platforms? The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples: **Financial services:** The use of AI in financial services is prolific, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With MLOps Plat, data science teams can build models with company data and deploy them to both internal and external applications. **Healthcare:** Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more. **Retail:** In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers. ### How to Buy MLOps Platforms #### Requirements Gathering (RFI/RFP) for MLOps Platforms If a company is just starting out and looking to purchase their first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option. The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, they must look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform. #### Compare MLOps Platforms **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. #### Selection of MLOps Platforms **Choose a selection team** Before getting started, creating a winning team that will work together throughout the entire process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities. **Negotiation** Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### What Do MLOps Platforms Cost? As mentioned above, MLOps Platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure.  As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will often not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle. Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software. #### Return on Investment (ROI) Businesses decide to deploy MLOps Platforms to derive some degree of ROI. As they are looking to recoup the losses from the software, it is critical to understand its costs. As mentioned above, these platforms are typically billed per user, sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform. ### Implementation of MLOps Platforms **How are MLOps Platforms Implemented?** Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively. **Who is Responsible for MLOps Platforms Implementation?** It may require a lot of people, or many teams, to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a complete understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management. **What Does the Implementation Process Look Like for MLOps Platforms?** In terms of implementation, it is typical for the platform deployment to begin in a limited fashion and subsequently roll out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site to better understand how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business). If the deployment was not successful, the team could go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data as a whole. **When Should You Implement MLOps Platforms?** As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must prioritize getting their data in order, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output.  ### MLOps Platforms Trends **AutoML** AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more. **Embedded AI** Machine and deep learning functionality are getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. Using embedded AI inside software like CRM, marketing automation, and analytics solutions allows users to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected. **Machine Learning as a service (MLaaS)** The software environment has moved to a more granular, microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies offer MLaaS to other businesses. Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as businesses continue to rely on these microservices and as the need for AI increases. **Explainability** When it comes to machine learning algorithms, especially deep learning, it may be particularly difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency, becomes crucial. MLOps Platforms are increasingly including tools for explainability, helping users build explainability into their models and meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.