# Best MLOps Platforms - Page 8 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* 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. ## How Many MLOps Platforms Products Does G2 Track? **Total Products under this Category:** 250 ### Category Stats (May 2026) - **Average Rating**: 4.5/5 (↑0.01 vs Apr 2026) - **New Reviews This Quarter**: 55 - **Buyer Segments**: Small-Business 54% │ Mid-Market 31% │ Enterprise 14% - **Top Trending Product**: Cloudera Data Platform (+0.155) *Last updated: May 18, 2026* ## How Does G2 Rank MLOps Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 6,700+ Authentic Reviews - 250+ 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:** [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - **Highest Performer:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews) - **Easiest to Use:** [Roboflow](https://www.g2.com/products/roboflow/reviews) - **Top Trending:** [Arize AI](https://www.g2.com/products/arize-ai/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Alteryx Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. [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%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bplacement_resource_ids%5D%5B%5D=684&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&secure%5Bresource_id%5D=1910&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmlops-platforms%3Fpage%3D14%26source%3Dsearch&secure%5Btoken%5D=6066d35aca75346051c1a8e8c0d9e7b7a31305f8db3f4bed46470fc8cd909ac9&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial) --- ## What Are the Top-Rated MLOps Platforms Products in 2026? ### 1. [Kognic](https://www.g2.com/products/kognic/reviews) Kognic is the leader in autonomy data annotation, delivering the world's most productive platform for multi-modal sensor-fusion data. Purpose-built for cameras, LiDAR, radar, and temporal streams, Kognic helps autonomy teams accelerate development with premium quality and high-throughput workflows. Our unique advantage combines three elements: People — domain experts and a scalable global workforce operating under strict ethical standards; Platform — designed to minimize human effort, integrate automation, and optimize productivity; Processes — proven workflows for quality assurance, scale, and predictability. Together, this makes Kognic the price leader in autonomy data annotation — no one delivers more annotated autonomy data per dollar. Trusted by enterprise customers across the U.S., Europe, China, and Japan, Kognic has delivered over 100 million annotations with full ISO, SOC2, and TISAX certifications. We support flexible deployment models — cloud (SaaS), on-premise, or hybrid — and seamlessly integrate with customer ML pipelines, cloud storage (AWS, Azure, GCP), and frameworks like PyTorch and TensorFlow. From bounding boxes to trajectory evaluation, intent judging, and clip curation, Kognic adapts workflows to meet emerging autonomy needs. We evolve with the frontier of Physical AI, ensuring customers get the most annotated autonomy data for their budget. **Who Is the Company Behind Kognic?** - **Seller:** [Kognic](https://www.g2.com/sellers/kognic) - **Year Founded:** 2018 - **HQ Location:** Gothenburg, SE - **LinkedIn® Page:** https://www.linkedin.com/company/kognic/ (107 employees on LinkedIn®) ### 2. [KubeHA](https://www.g2.com/products/kubeha/reviews) KubeHA is a GenAI-powered SaaS platform designed to streamline the monitoring, observability, remediation, and exploration (MORE) of Kubernetes environments. Tailored for Site Reliability Engineering (SRE) and DevOps teams, KubeHA consolidates essential functionalities into a single interface, enhancing operational efficiency and system reliability. Key Features and Functionality: - Monitoring: Provides real-time, high-fidelity Kubernetes metrics and alerts, with auto-analysis and correlated Prometheus graphs for comprehensive infrastructure and application insights. - Observability: Offers unified visibility across logs, metrics, traces, and errors, incorporating built-in anomaly detection and smart correlation to swiftly identify performance issues and root causes. - Remediation: Utilizes AI-powered mega analysis of cluster states and changes, delivering smart fix suggestions and enabling one-click remediation to resolve issues promptly. - Exploration: Features KubeHA GPT, allowing users to ask questions in plain English and receive instant, actionable answers regarding alerts, logs, metrics, and more. Primary Value and Problem Solved: KubeHA addresses the complexity of managing Kubernetes environments by integrating monitoring, observability, remediation, and exploration into a cohesive platform. This consolidation eliminates the need for multiple tools, reducing operational overhead and enhancing system reliability. By leveraging GenAI, KubeHA automates alert analysis and remediation, significantly decreasing manual intervention and improving response times. Its user-friendly interface and seamless integrations with popular tools empower teams to maintain high availability and performance with minimal effort. **Who Is the Company Behind KubeHA?** - **Seller:** [KubeHA](https://www.g2.com/sellers/kubeha) - **Year Founded:** 2018 - **HQ Location:** Morris Plains, US - **LinkedIn® Page:** https://www.linkedin.com/showcase/kubeha-ara/ (1 employees on LinkedIn®) ### 3. [Lablup](https://www.g2.com/products/lablup/reviews) Lablup Inc., established in 2015, is a technology company dedicated to making artificial intelligence (AI) accessible and scalable. Their flagship product, Backend.AI, is a hyperscale AI infrastructure operating platform designed to simplify, integrate, and accelerate AI training and inference processes. By leveraging proprietary container-level GPU virtualization, Backend.AI enables multiple users and workloads to share GPU resources efficiently, thereby reducing costs and maximizing utilization. This platform supports a wide range of AI accelerators and offers smart scaling for AI inference, facilitating seamless orchestration of compute resources across on-premises, cloud, and hybrid environments. Backend.AI is widely adopted across various industries, including finance, healthcare, research, and automotive, providing an easy-to-use yet powerful full-stack environment for AI infrastructure. Key Features and Functionality: - Container-Based Computing Cluster Platform: Backend.AI offers a flexible cluster backend tailored for AI frameworks, supporting scales from single PCs to large-scale infrastructures. - GPU Virtualization and Fractional GPU Allocation: The platform utilizes proprietary container-level GPU virtualization, allowing multiple users and workloads to share GPU resources efficiently, reducing costs and maximizing utilization. - Support for Various Computing and ML Frameworks: Backend.AI supports a range of AI accelerators and offers smart scaling for AI inference, facilitating seamless orchestration of compute resources across on-premises, cloud, and hybrid environments. - Web-Based User Interface: The platform provides an intuitive web UI for managing resources, monitoring performance, and visualizing data, enhancing user experience and operational efficiency. - RESTful APIs and Client Libraries: Backend.AI offers RESTful APIs and client libraries for seamless integration with existing tools and workflows, ensuring flexibility and adaptability. - Scalable Architecture: Designed to handle large datasets and complex models, the platform's scalable architecture meets the demands of extensive computational tasks. - Security Features: Backend.AI includes role-based access control and security features to protect sensitive data, ensuring compliance and data integrity. Primary Value and User Solutions: Backend.AI addresses the challenges AI developers and researchers face in efficiently managing and scaling computing resources. By providing a streamlined, container-based computing cluster platform with GPU virtualization and resource management capabilities, it enables users to build, train, and serve AI models of any size efficiently. The platform's support for various computing and machine learning frameworks, along with its scalable architecture, allows organizations to shorten time-to-deployment and reduce total cost of ownership. Backend.AI's seamless orchestration of compute resources across different environments empowers enterprises to focus on innovation and development without the complexities of infrastructure management. **Who Is the Company Behind Lablup?** - **Seller:** [Lablup](https://www.g2.com/sellers/lablup) - **Year Founded:** 2015 - **HQ Location:** Seoul, KR - **LinkedIn® Page:** https://www.linkedin.com/company/lablup (40 employees on LinkedIn®) ### 4. [Latona](https://www.g2.com/products/latona/reviews) Latona Empowers IoX from a piece of AI. **Who Is the Company Behind Latona?** - **Seller:** [Latona](https://www.g2.com/sellers/latona) - **Year Founded:** 2018 - **HQ Location:** Shibuya, JP - **LinkedIn® Page:** https://www.linkedin.com/company/latonaio/ (4 employees on LinkedIn®) ### 5. [LayerNext](https://www.g2.com/products/layernext/reviews) LayerNext is an AI CFO platform that keeps your finances accurate, up to date, and ready for decisions in real time. We automatically categorize transactions, reconcile accounts, and maintain clean books by integrating directly with QuickBooks. On top of accurate financial data, LayerNext delivers CFO-level insights including burn rate, runway, cash flow trends, margin analysis, and forward-looking financial signals. No spreadsheets. No manual work. No delays. Our platform combines advanced AI with human-level precision, giving founders and small businesses a reliable way to understand their financial health without hiring or managing bookkeeping staff. What LayerNext does: - Automated bookkeeping - Real-time reconciliation - Cash flow and runway insights - Burn and spending analysis - Instant financial reports LayerNext gives business owners clarity, control, and confidence, so they can focus on running the business, not maintaining the books. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate LayerNext?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind LayerNext?** - **Seller:** [LayerNext AI](https://www.g2.com/sellers/layernext-ai) - **Year Founded:** 2022 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/layernext/ (7 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 6. [llmonitor](https://www.g2.com/products/llmonitor/reviews) LLMonitor is an open-source observability and evaluation toolkit designed for AI developers to monitor, analyze, and optimize their applications effectively. It provides comprehensive monitoring capabilities, allowing developers to track the performance of AI models in real-time, ensuring optimal functionality. The user-friendly interface is designed for ease of use, enabling developers to focus on building rather than troubleshooting. Customizable dashboards allow for a tailored monitoring experience to fit specific needs. Additionally, LLMonitor benefits from a vibrant community of developers who contribute to the toolkit, ensuring continuous improvement and innovation. Key Features: - Comprehensive Monitoring: Track the performance of AI models in real-time to ensure optimal functionality. - User-Friendly Interface: Designed for ease of use, allowing developers to focus on building rather than troubleshooting. - Customizable Dashboards: Tailor your monitoring experience with dashboards that fit your specific needs. - Community Support: Benefit from a vibrant community of developers who contribute to the toolkit, ensuring continuous improvement and innovation. Primary Value: LLMonitor empowers AI developers by providing a robust, open-source solution for monitoring and evaluating AI applications. By integrating LLMonitor into their workflow, developers can gain insights into model performance, identify bottlenecks, and make data-driven decisions to enhance their applications. The flexibility and transparency of the open-source nature allow for modifications and enhancements according to project requirements, making it an invaluable asset in the fast-paced AI landscape. **Who Is the Company Behind llmonitor?** - **Seller:** [llmonitor](https://www.g2.com/sellers/llmonitor) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 7. [llm.report](https://www.g2.com/products/llm-report/reviews) Llm.report is a free OpenAI API analytics dashboard, offering clear insights into your AI usage. **Who Is the Company Behind llm.report?** - **Seller:** [llm.report](https://www.g2.com/sellers/llm-report) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 8. [LocalOps](https://www.g2.com/products/localops/reviews) LocalOps is a modern cloud neutral Internal Developer Platform for lean engineering teams using AWS/GCP/Azure and with DevOps bottlenecks. Teams can migrate to cloud (AWS) 6x-10x faster and get self-serve access to cloud infrastructure for day to day deployments, automated CI/CD pipeline from Github, pre-configured open source monitoring stack and more. No terraform / DevOps skillset required. **Who Is the Company Behind LocalOps?** - **Seller:** [LocalOps](https://www.g2.com/sellers/localops) - **Year Founded:** 2023 - **HQ Location:** Chennai, IN - **LinkedIn® Page:** https://www.linkedin.com/company/localops-inc (5 employees on LinkedIn®) ### 9. [Lucidic AI](https://www.g2.com/products/lucidic-ai/reviews) Lucidic AI is a comprehensive training platform designed to enhance the reliability and performance of AI agents. By enabling developers to parameterize agentic systems and conduct extensive simulations, Lucidic AI facilitates iterative improvements without the need to adjust model weights. This approach ensures that AI agents operate effectively across diverse scenarios, leading to more dependable and efficient deployments. Key Features and Functionality: - Custom Reward Definition: Allows users to define objective functions aligned with domain-specific metrics such as inference latency and computational cost, ensuring that AI agents are optimized for specific operational goals. - Intelligent Candidate Exploration: Automatically searches through numerous agent configurations—including prompt variations, tool orderings, and context strategies—to identify the most effective setups, streamlining the development process. - Continuous Improvement in Production: Facilitates ongoing enhancement of AI agents by enabling continuous training and adaptation based on real-world performance data, ensuring sustained reliability and effectiveness. - Evaluation Rubrics: Provides structured evaluation criteria to score or validate agent behavior across sessions, capturing nuanced behaviors and enforcing domain-specific quality standards. - Mass Simulations: Offers the capability to run multiple simulations concurrently, providing analytics that reveal patterns, inconsistencies, and performance metrics at scale, which is essential for robust agent development. Primary Value and Problem Solved: Lucidic AI addresses the critical challenge of deploying reliable AI agents by offering tools that enable developers to systematically test, evaluate, and refine agent behaviors. By providing mechanisms for custom reward definitions, intelligent exploration of configurations, and continuous improvement, Lucidic AI ensures that AI agents meet specific performance metrics and operate consistently across various scenarios. This leads to increased trust in AI deployments, reduced risk of unexpected behaviors, and more efficient development cycles, ultimately enhancing the overall effectiveness of AI-driven solutions. **Who Is the Company Behind Lucidic AI?** - **Seller:** [Lucidic AI](https://www.g2.com/sellers/lucidic-ai) - **Year Founded:** 2025 - **HQ Location:** N/A - **LinkedIn® Page:** https://linkedin.com/company/lucidic-ai (780 employees on LinkedIn®) ### 10. [MakinaRocks](https://www.g2.com/products/makinarocks/reviews) Runway™ is a hyperconnected enterprise MLOps solution designed to simplify and scale AI operations across diverse industries. It empowers organizations to Connect, Automate, and Govern AI workflows—right from data ingestion to model deployment and monitoring **Who Is the Company Behind MakinaRocks?** - **Seller:** [MakinaRocks](https://www.g2.com/sellers/makinarocks) - **Year Founded:** 2017 - **HQ Location:** Seoul, KR - **LinkedIn® Page:** https://www.linkedin.com/company/makinarocks (80 employees on LinkedIn®) ### 11. [Minitab Model Ops](https://www.g2.com/products/minitab-model-ops/reviews) Minitab Model Ops is a centralized platform for deploying, monitoring, and managing analytical and machine learning models at scale. It enables organizations to operationalize models built in Minitab and other environments, ensuring they deliver consistent, reliable insights in real-world applications. With Minitab Model Ops, teams can automate model deployment, track model performance over time, and monitor for drift or degradation. Built-in governance and version control help ensure transparency, compliance, and reproducibility across the model lifecycle. By bridging the gap between model development and production, Minitab Model Ops helps organizations accelerate time to value, maintain trust in their analytics, and make faster, data-driven decisions. **Who Is the Company Behind Minitab Model Ops?** - **Seller:** [Minitab](https://www.g2.com/sellers/minitab-14ca02fe-fdeb-44c4-b0db-904058d0221b) - **Year Founded:** 1972 - **HQ Location:** State College, Pennsylvania, United States - **Twitter:** @Minitab (5,019 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/39142/ (703 employees on LinkedIn®) ### 12. [MLflow](https://www.g2.com/products/mlflow-mlflow/reviews) MLflow is an open-source platform designed to streamline the end-to-end machine learning (ML) lifecycle, addressing challenges in model development, deployment, and management. It offers a suite of tools that enhance collaboration among ML practitioners, ensuring that projects are robust, transparent, and ready for real-world applications. Key Features and Functionality: - Experiment Tracking: Provides APIs and a user interface to log parameters, code versions, metrics, and artifacts during the ML process, facilitating easy comparison of multiple runs across different users. - Model Registry: Offers a centralized model store with APIs and a UI to manage the full lifecycle of MLflow Models, including versioning, aliasing, tagging, and annotations. - MLflow Deployments for LLMs: Equipped with standardized APIs, this server streamlines access to both SaaS and open-source large language models (LLMs), enhancing security through authenticated access. - Evaluate: Provides tools for in-depth model analysis, enabling objective comparison of models, whether they are traditional ML algorithms or cutting-edge LLMs. - Prompt Engineering UI: A dedicated environment for prompt engineering, allowing for experimentation, refinement, evaluation, testing, and deployment of prompts. - Recipes: Guides for structuring ML projects, focusing on delivering functional end results optimized for real-world deployment scenarios. - Projects: Standardizes the packaging of ML code, workflows, and artifacts, employing descriptors or conventions to define dependencies and execution methods. Primary Value and Problem Solved: MLflow addresses the complexities inherent in the ML lifecycle by providing a unified platform that ensures efficiency, consistency, and traceability. By integrating core components like experiment tracking, model registry, and deployment tools, MLflow enables teams to navigate the intricate processes of model development and management seamlessly. This comprehensive approach fosters innovation, enhances collaboration, and accelerates the deployment of high-quality ML solutions. **Who Is the Company Behind MLflow?** - **Seller:** [Mlflow](https://www.g2.com/sellers/mlflow) - **Year Founded:** 2018 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/mlflow-org (30 employees on LinkedIn®) ### 13. [mlop](https://www.g2.com/products/mlop/reviews) mlop is an open-source platform designed to streamline the tracking, optimization, and collaboration of machine learning experiments. Tailored for modern teams, it offers a comprehensive suite of tools that enhance the efficiency and effectiveness of ML operations. By providing real-time insights and actionable recommendations, mlop empowers users to improve model performance and reduce computational costs. Key Features and Functionality: - Experiment Tracking: Monitor and compare experiments with detailed metrics, parameters, and artifacts, ensuring a complete history of model development. - Advanced Visualization: Utilize interactive dashboards for real-time monitoring of training progress and model performance analysis. - Real-Time Alerts: Receive immediate notifications about model performance issues, enabling prompt interventions to prevent resource wastage. - Seamless Integration: Easily connect with existing codebases and development workflows, including compatibility with tools like PyTorch Lightning and Hugging Face Transformers. - Open Source and High Performance: Built for speed and scalability, mlop handles large datasets and complex models efficiently, with a community-driven development approach. Primary Value and Solutions: mlop addresses the challenges of managing machine learning experiments by offering a centralized platform that not only logs metrics but also provides actionable insights to enhance model performance. Its real-time alerting system helps users identify and resolve issues promptly, saving valuable computational resources. The platform's seamless integration capabilities ensure that teams can incorporate mlop into their existing workflows without disruption. By focusing on performance and user-friendly design, mlop enables machine learning teams to build, track, and deploy models with confidence and efficiency. **Who Is the Company Behind mlop?** - **Seller:** [mlop](https://www.g2.com/sellers/mlop) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/mlop (28 employees on LinkedIn®) ### 14. [MLTest](https://www.g2.com/products/mltest/reviews) The World’s Most Advanced AI Security Platform Protect your organization from any AI threat with the security platform of choice for enterprises around the world. **Who Is the Company Behind MLTest?** - **Seller:** [Lakera](https://www.g2.com/sellers/lakera) - **Year Founded:** 2021 - **HQ Location:** San Francisco, US - **Twitter:** @LakeraAI (1,683 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/lakeraai/ (75 employees on LinkedIn®) ### 15. [Modzy](https://www.g2.com/products/modzy/reviews) Modzy accelerates the deployment, integration, and governance of production-ready AI. With integrations for the leading data science and DevOps tools, teams count on Modzy to quickly and easily build AI-enabled applications in a standard, repeatable, and secure way. By leveraging Modzy as a central location for monitoring all AI across the enterprise or at the edge, leaders can establish governance and security while generating higher returns from AI: 🚀 Get your AI initiatives to market faster by launching new models in minutes, not months 💰 Cut your AI cloud costs by right-sizing your AI hardware footprint 🛡️ Protect your AI models from cyber threats and sophisticated adversarial attacks ☁️ Stop slow, expensive data transfers by running your AI workloads anywhere: In the cloud, at the data center, or at the edge Modzy was founded in 2019 with a clear purpose: Build a world where people and machines working together—outperform either working alone.​ Our company, based in Vienna, VA, serves fortune 500 customers in energy and utilities, telecom, manufacturing, life sciences, and the public sector. **Who Is the Company Behind Modzy?** - **Seller:** [Modzy](https://www.g2.com/sellers/modzy) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 16. [Monitaur](https://www.g2.com/products/monitaur/reviews) Model governance software for AI, statistical models, and everything in between. Your business needs a consistent approach to model governance for informed decisions, risk management, and performance visibility. With Monitaur, you can implement responsible and ethical governance from policy to proof for internal and external stakeholders, including technical teams, business owners, regulators, and auditors. **Who Is the Company Behind Monitaur?** - **Seller:** [Monitaur](https://www.g2.com/sellers/monitaur) - **Year Founded:** 2019 - **HQ Location:** Boston, Massachusetts, United States - **LinkedIn® Page:** https://www.linkedin.com/company/monitaur (29 employees on LinkedIn®) ### 17. [Mutatio](https://www.g2.com/products/mutatio/reviews) Mutatio is a cutting-edge platform designed to streamline the process of managing and deploying machine learning models. It offers a comprehensive suite of tools that facilitate the entire machine learning lifecycle, from model development to deployment and monitoring. By providing an intuitive interface and robust infrastructure, Mutatio empowers data scientists and engineers to focus on building high-quality models without the complexities of deployment logistics. Key Features and Functionality: - Model Deployment: Simplifies the deployment of machine learning models into production environments, ensuring scalability and reliability. - Version Control: Maintains a history of model versions, allowing for easy rollback and comparison between different iterations. - Monitoring and Logging: Provides real-time monitoring and logging capabilities to track model performance and detect anomalies. - Integration Support: Offers seamless integration with popular machine learning frameworks and tools, enhancing workflow efficiency. - User-Friendly Interface: Features an intuitive dashboard that simplifies the management of models and resources. Primary Value and Problem Solved: Mutatio addresses the challenges associated with deploying and managing machine learning models in production. By automating complex processes and providing a centralized platform, it reduces the time and effort required to transition models from development to deployment. This enables organizations to accelerate their machine learning initiatives, improve model performance monitoring, and ensure consistent delivery of AI-driven solutions. **Who Is the Company Behind Mutatio?** - **Seller:** [Mutatio](https://www.g2.com/sellers/mutatio) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 18. [NannyML](https://www.g2.com/products/nannyml/reviews) NannyML is an open-source Python library designed to monitor and maintain the performance of machine learning models in production environments, even when ground truth labels are delayed or unavailable. By estimating model performance without access to target data, NannyML enables data scientists and ML engineers to detect and address issues such as data drift and concept drift, ensuring that models continue to deliver accurate and reliable predictions over time. Key Features and Functionality: - Performance Estimation Without Labels: Utilizes advanced statistical techniques like Confidence-Based Performance Estimation (CBPE) and Direct Loss Estimation (DLE) to estimate classification and regression metrics in real-time, without requiring actual outcomes. - Data Drift Detection: Monitors changes in input data distributions at both feature and dataset levels, employing metrics such as Jensen-Shannon divergence and Wasserstein distance to identify shifts that may impact model performance. - Concept Drift Detection: Measures the impact of concept drift on model performance, providing insights into when retraining may be necessary. - Root Cause Analysis: Links data drift alerts to specific features, enabling rapid identification of the factors contributing to performance degradation. - Business Impact Assessment: Allows users to define cost-benefit matrices to quantify the monetary or business-oriented outcomes of model performance, ensuring alignment with organizational objectives. - Automated Monitoring and Alerting: Provides intelligent alerting systems that focus on performance-impacting issues, reducing false alarms and alert fatigue. - Integration and Deployment: Offers seamless integration with existing MLOps pipelines and can be deployed within an organization's cloud infrastructure for enhanced security and control. Primary Value and Problem Solved: NannyML addresses the critical challenge of maintaining machine learning model performance in production settings where ground truth labels are often delayed or absent. By enabling real-time performance estimation and drift detection without the need for actual outcomes, NannyML empowers data science teams to proactively identify and resolve issues that could lead to model degradation. This proactive monitoring ensures that models continue to provide accurate predictions, thereby preserving their business value and supporting informed decision-making processes. Additionally, by reducing false alarms and focusing on performance-impacting changes, NannyML helps teams avoid alert fatigue and allocate resources more effectively. **Who Is the Company Behind NannyML?** - **Seller:** [NannyML](https://www.g2.com/sellers/nannyml) - **HQ Location:** Leuven, BE - **LinkedIn® Page:** https://www.linkedin.com/company/nannyml (32 employees on LinkedIn®) ### 19. [Netra](https://www.g2.com/products/keyvalue-software-systems-netra/reviews) Netra is an end-to-end AI observability, evaluation, and simulation platform that gives engineering teams complete visibility into every decision their AI agents make, from development through production. It is purpose-built for multi-step, multi-agent, and multi-tool workflows where traditional APM and LLM monitoring tools fall short. The platform is organized around four core capabilities. Observability delivers full-fidelity tracing across every LLM call, tool invocation, reasoning step, and retrieval, with real-time cost, latency, and error tracking. Evaluation enables teams to score agent quality automatically on every decision using built-in rubrics, custom LLM-as-judge evaluators, and code evaluators, with online evals running continuously on live traffic. Simulation lets teams stress-test agents against thousands of real and synthetic scenarios before production, using diverse personas and A/B comparisons against a baseline. Prompt Management provides a centralized workspace where every prompt is versioned, lineage-tracked, and rollback-safe, with every production response traceable back to its exact prompt version. Netra is built on OpenTelemetry, making it compatible with any OTLP-compliant backend and ensuring teams can get started with just 2 to 3 lines of code. It integrates with 14+ LLM providers including OpenAI, Anthropic, Google Gemini, and AWS Bedrock, and 12+ AI frameworks including LangChain, LangGraph, CrewAI, and LlamaIndex. The platform is SOC2 Type II certified and compliant with GDPR and HIPAA, with strict US and EU data residency and zero cross-region data sharing. Enterprise teams get on-premise deployment, isolated databases, and SSO. Available on a Free plan with no credit card required, a Pro plan at $39 per month, and custom Enterprise pricing. **Who Is the Company Behind Netra?** - **Seller:** [KeyValue Software Systems](https://www.g2.com/sellers/keyvalue-software-systems-36b38222-8354-45bc-9485-8258e99a8ea2) - **Year Founded:** 2015 - **HQ Location:** Kochi, IN - **LinkedIn® Page:** https://www.linkedin.com/company/keyvaluesystems (522 employees on LinkedIn®) ### 20. [Neuralvault](https://www.g2.com/products/neuralvault/reviews) NeuralVault is a comprehensive suite of generative AI tools designed to provide specialized solutions across various industries. By integrating advanced AI technologies, NeuralVault empowers professionals to make informed decisions with enhanced precision and efficiency. Key Features and Functionality: - Legal Document Analysis GPT: Utilizes advanced natural language processing to interpret legal language, identify key clauses, and offer insights, streamlining the review process for legal professionals. - Fashion Authenticity GPT: Analyzes design details, materials, and branding elements to distinguish authentic luxury fashion items from counterfeits, aiding collectors and enthusiasts in verifying product authenticity. - Art Valuation and Authentication GPT: Assesses artistic details, historical data, and market trends to provide accurate art valuations and authentication, supporting collectors and dealers in making informed decisions. - Value Investing GPT: Applies value investing principles to analyze stocks, identifying undervalued assets with strong fundamentals, serving as a strategic tool for long-term wealth building. - Equity Crowdfunding Investment Analysis GPT: Evaluates crowdfunding ventures by assessing market potential and business viability, assisting investors in navigating the dynamic crowdfunding landscape. - Crowdfunding Campaign Optimization GPT: Offers strategic advice on campaign structure, rewards, and engagement to maximize the success of crowdfunding campaigns, benefiting entrepreneurs seeking effective fundraising strategies. Primary Value and User Solutions: NeuralVault addresses the need for specialized, AI-driven tools that cater to niche market requirements. By offering a diverse array of GPTs, it enables professionals to enhance accuracy, efficiency, and insight in their respective fields. Whether it's simplifying legal document analysis, authenticating luxury fashion items, valuing art pieces, identifying investment opportunities, or optimizing crowdfunding campaigns, NeuralVault provides tailored solutions that revolutionize decision-making processes. With a subscription model priced at $5 per month, it ensures that cutting-edge AI technology is both accessible and affordable, allowing users to stay at the forefront of AI-driven advancements in their industries. **Who Is the Company Behind Neuralvault?** - **Seller:** [NeuralVault](https://www.g2.com/sellers/neuralvault) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 21. [Neysa Velocis](https://www.g2.com/products/neysa-velocis/reviews) Neysa Velocis is a full-stack AI Acceleration Cloud - built for teams that build AI. It unifies your entire AI workflow, from training and fine-tuning to inference and deployment, in one system engineered for predictable performance, enterprise governance, and model security. - GPU-as-a-Service: Get access to NVIDIA B300, H200 SXM, H100 NVL, H100 SXM, L40S, L4, and AMD MI300X. Deploy as Virtual Machines, Managed Kubernetes Containers, or Bare Metal clusters. We offer them in reserved plans (12–36 month commit). - AI Platform-as-a-Service (AI PaaS): Pre-configured development environments: Jupyter, PyTorch, TensorFlow, Hugging Face, MLflow, Kubeflow Pipelines, Docker, and Git - ready to launch in minutes. Model registries, experiment tracking, CI/CD for ML, data lake creation, and DBaaS built in. - Inference-as-a-Service: Deploy open-source models (Llama, DeepSeek, Mistral, Qwen, Gemma, Mixtral) as managed inference endpoints. Custom APIs for OCR, NLP, Computer Vision, and real-time stream processing. Move from training to production with fewer handoffs. - Real-Time Monitoring: Unified observability dashboard with granular, real-time GPU utilization, disk, and NVMe metrics. Custom metrics configurable per workload. Aegis LLM Shield: Sits inline on every inference endpoint. Blocks prompt injection and jailbreaks, redacts PII in model outputs, enforces content policies, and covers OWASP LLM Top 10 risks - without changing your application code. Available as a native add-on. - AI Infrastructure Security: Zero-trust architecture. Granular RBAC by project, persona, and asset. BYOK with enterprise KMS support. Full audit logs, governance-ready and exportable. Certified and compliant by default. ISO/IEC 27001:2022, ISO 27017:2015, ISO 27018:2019, SOC2 Type II, IRDAI. Aligned to GDPR, HIPAA, India's DPDP Act, and PCI-DSS. **Who Is the Company Behind Neysa Velocis?** - **Seller:** [Neysa](https://www.g2.com/sellers/neysa) - **Company Website:** https://neysa.ai/ - **Year Founded:** 2023 - **HQ Location:** Mumbai, IN - **LinkedIn® Page:** https://www.linkedin.com/company/neysaai (90 employees on LinkedIn®) ### 22. [Nodes AI](https://www.g2.com/products/nodes-ai/reviews) Nodes AI is an advanced artificial intelligence platform designed to streamline and enhance the development and deployment of AI models. It offers a comprehensive suite of tools that cater to both novice and experienced developers, enabling efficient model training, evaluation, and integration into various applications. By providing an intuitive interface and robust backend support, Nodes AI simplifies the complexities associated with AI development, allowing users to focus on innovation and problem-solving. Key Features and Functionality: - User-Friendly Interface: An intuitive dashboard that facilitates easy navigation and management of AI projects. - Model Training and Evaluation: Comprehensive tools for training AI models with various datasets and evaluating their performance to ensure optimal results. - Scalability: Supports scalable solutions, allowing users to handle projects of varying sizes and complexities without compromising performance. - Integration Capabilities: Seamless integration with existing systems and applications, enabling smooth deployment of AI models into production environments. - Collaboration Tools: Facilitates team collaboration with shared workspaces and version control, ensuring efficient project management. Primary Value and User Solutions: Nodes AI addresses the challenges of AI development by providing a platform that simplifies the process from model creation to deployment. It reduces the technical barriers often associated with AI projects, making it accessible to a broader audience. By offering scalable and integrative solutions, Nodes AI empowers businesses and developers to harness the power of artificial intelligence, leading to enhanced decision-making, increased efficiency, and the ability to tackle complex problems with innovative solutions. **Who Is the Company Behind Nodes AI?** - **Seller:** [Nodes AI](https://www.g2.com/sellers/nodes-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 23. [Nota AI](https://www.g2.com/products/nota-ai/reviews) Nota AI is a technology company specializing in AI optimization solutions, enabling businesses to deploy efficient and high-performance AI models across various platforms. Their flagship product, NetsPresso, is an end-to-end platform that streamlines the development and deployment of AI models, making AI more accessible and cost-effective for organizations. Key Features and Functionality: - Model Compression: Reduces the size of AI models without compromising performance, facilitating deployment on resource-constrained devices. - Automated Optimization: Utilizes machine learning techniques to automatically enhance model efficiency and speed. - Hardware Compatibility: Ensures optimized models are compatible with a wide range of hardware, from cloud servers to edge devices. - User-Friendly Interface: Provides an intuitive platform for users to develop, optimize, and deploy AI models with minimal technical expertise. Primary Value and Problem Solved: Nota AI addresses the challenge of deploying complex AI models in environments with limited computational resources. By offering tools that compress and optimize models, they enable businesses to implement AI solutions that are both effective and efficient, reducing costs and expanding the applicability of AI technologies across various industries. **Who Is the Company Behind Nota AI?** - **Seller:** [Nota AI](https://www.g2.com/sellers/nota-ai) - **Year Founded:** 2015 - **HQ Location:** Seoul, KR - **LinkedIn® Page:** https://www.linkedin.com/company/nota-incorporated (160 employees on LinkedIn®) ### 24. [NVIDIA Run:ai](https://www.g2.com/products/nvidia-run-ai/reviews) NVIDIA Run:ai is a Kubernetes-native platform designed to orchestrate AI workloads and optimize GPU resources. Tailored for machine learning and AI teams, it streamlines resource management, enhances GPU utilization, and accelerates development cycles. By dynamically allocating GPU resources and integrating seamlessly with leading MLOps tools and cloud environments, Run:ai ensures efficient and scalable AI operations. Key Features and Functionality: - Dynamic GPU Scheduling: Automatically allocates GPU resources based on workload demands, ensuring optimal utilization and minimizing idle time. - Fractional GPU Allocation: Enables multiple workloads to share a single GPU, allowing for efficient resource distribution and cost savings. - Automated Workload Orchestration: Manages the deployment and scaling of AI workloads, simplifying complex processes and reducing manual intervention. - Team-Based Resource Governance: Implements role-based access control and team-level quotas to ensure resource isolation, compliance, and visibility across AI teams. - Seamless Integration with AWS Services: Deploys alongside Amazon EKS and integrates with services like Amazon S3, CloudWatch, and IAM for a unified operational experience. - MLOps Workflow Compatibility: Supports tools such as JupyterHub, Kubeflow, and MLflow, facilitating end-to-end machine learning pipelines. Primary Value and Problem Solved: NVIDIA Run:ai addresses the challenge of efficiently managing and scaling AI workloads by optimizing GPU resource utilization. It eliminates the inefficiencies of static GPU allocation through dynamic scheduling and fractional sharing, leading to higher throughput and faster model development. By providing a centralized platform for resource management, Run:ai empowers organizations to accelerate AI initiatives, reduce operational costs, and maintain tight control over infrastructure, thereby driving innovation without the complexities of manual resource management. **Who Is the Company Behind NVIDIA Run:ai?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,228,514 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN ### 25. [Ocular AI](https://www.g2.com/products/ocular-ai/reviews) Ocular AI is the Multimodal AI Data Lakehouse. With Ocular, AI teams can seamlessly ingest, catalog/curate, search, annotate, and train on video, image, and audio data — all on one AI-native platform. Built for speed, scale, and accuracy, Ocular transforms petabytes of raw, unstructured data into high-quality datasets and production-grade custom models, enabling the next generation of multimodal AI. Whether you're building computer vision systems, robotics perception models, or domain-specific generative AI, Ocular provides everything you need to go from data to model — fast. Ocular Foundry — The Multimodal Lakehouse for AI Foundry is a multimodal data lakehouse purpose-built for unstructured data workflows. It combines powerful infrastructure, intuitive tooling, and AI-native workflows into one cohesive platform. Ingest, Catalog, & Curate — Bring all your unstructured data into a single, unified platform. Foundry supports direct integrations with cloud storage, SDKs, APIs, and more to centralize enterprise-scale video, image, and audio datasets. Visualize and curate your data using embedding-powered interfaces for smarter, label-prioritized workflows. Search & Understand — Use natural language to search across petabytes of video and image data. Ask complex queries like “Show forklifts near a dock” or “Find red cars at night,” and Foundry will pinpoint exact frames and timestamps. The platform understands scenes, detects actions, reads embedded text, and locates key events across modalities. Annotate & Label with Agents & Humans— Supercharge annotation workflows with AI Data Agents, fine-tuned models, and human-in-the-loop collaboration. Use advanced tools for bounding boxes, segmentation, audio labeling, and frame-level tagging — all with project-specific ontologies and automated QA checks. Train & Evaluate — Fine-tune and evaluate custom models directly inside Foundry with integrated GPU-powered training. Track data lineage, monitor label coverage, and assess model readiness in real time with rich analytics and visual dashboards — no context switching or pipeline fragmentation. Foundry is the infrastructure layer built for teams solving hard AI problems with real-world, messy data. Bolt — Expert-in-the-Loop Annotation at Scale Bolt is Ocular’s high-precision annotation service designed for enterprises that need fast, accurate, domain-specific labeling. Unlike crowdwork platforms, Bolt is powered by trained professionals — engineers, medical experts, and QA specialists — to ensure every label meets your model’s unique requirements. With Bolt, you get: - Scalable annotations across video, image, and audio data - Expert-in-the-loop workflows for critical edge cases - Tight integration with Foundry for seamless project execution - Speed and accuracy without sacrificing context or quality Trusted by forward-thinking AI teams tackling the hardest multimodal AI problems. Ocular AI is SOC 2 compliant and designed to meet the security and performance demands of enterprise AI. Confidently build multimodal, production-ready models — all on one Multimodal Lakehouse. **Who Is the Company Behind Ocular AI?** - **Seller:** [Ocular AI](https://www.g2.com/sellers/ocular-ai) - **Year Founded:** 2024 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/use-ocular (6 employees on LinkedIn®) ## What Is MLOps Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to MLOps Platforms? - [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) --- ## 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.