# Best Data Science and Machine Learning Platforms - Page 5 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Data science and machine learning (DSML) platforms provide tools to build, deploy, and monitor machine learning (ML) algorithms by combining data with intelligent, decision-making models to support business solutions. These platforms may offer prebuilt algorithms and visual workflows for nontechnical users or require more advanced development skills for complex model creation. Core capabilities of data science and machine learning (DSML) software To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must: - Present a way for developers to connect data to algorithms so they can learn and adapt - Allow users to create ML algorithms and offer prebuilt algorithms for novice users - Provide a platform for deploying AI at scale How DSML software differs from other tools DSML platforms differ from traditional platform-as-a-service (PaaS) offerings by providing ML–specific functionality, such as prebuilt algorithms, model training workflows, and automated features that reduce the need for extensive data science expertise. Insights from G2 Reviews on DSML software According to G2 review data, users highlight the value of streamlined model development, ease of deployment, and options that support both nontechnical and advanced practitioners through visual interfaces or coding-based workflows. ## Top Data Science and Machine Learning Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,319 reviews) | Unified lakehouse ML and analytics workflows | "[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 (653 reviews) | End-to-end ML lifecycle with GCP-native MLOps | "[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 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (770 reviews) | End-to-end ML lifecycle with governed model deployment | "[Powerful Integration, Effortless Customization](https://www.g2.com/survey_responses/sas-viya-review-13109700)" | | 4 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (706 reviews) | SQL-native ML pipelines with unified data warehousing | "[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)" | | 5 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (212 reviews) | End-to-end ML workflows with no-code/code flexibility | "[Build Faster Workflows with Connected Data from many providers or distinct data sources](https://www.g2.com/survey_responses/dataiku-review-13120436)" | | 6 | [MATLAB](https://www.g2.com/products/matlab/reviews) | 4.5/5.0 (750 reviews) | Numerical simulation and ML algorithm prototyping | "[A Robust Powerhouse for Advanced Engineering Simulations and Modeling](https://www.g2.com/survey_responses/matlab-review-12689149)" | | 7 | [Hex](https://www.g2.com/products/hex-tech-hex/reviews) | 4.5/5.0 (401 reviews) | Polyglot SQL-Python notebooks with AI-assisted analysis | "[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)" | | 8 | [Deepnote](https://www.g2.com/products/deepnote/reviews) | 4.5/5.0 (379 reviews) | Collaborative notebook analytics with multi-source integration | "[Real-Time Collaboration That Makes Lab Work Easy](https://www.g2.com/survey_responses/deepnote-review-13100282)" | | 9 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Unified lakehouse analytics for hybrid AI workloads | "[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)" | | 10 | [Posit Team](https://www.g2.com/products/posit-team/reviews) | 4.5/5.0 (567 reviews) | Reproducible R and Python analytics workflows | "[Posit Team Makes Biostatistical Work Reproducible, Collaborative, and Secure](https://www.g2.com/survey_responses/posit-team-review-12977958)" | ## G2 Grid® for Data Science and Machine Learning Platforms ![G2 Grid® for Data Science and Machine Learning Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1327283&focus%5B%5D=10938&focus%5B%5D=24457&focus%5B%5D=162504&focus%5B%5D=7150&focus%5B%5D=1308796) Highlighted products: Databricks, Gemini Enterprise Agent Platform, SAS Viya, Snowflake, MATLAB, Hex, Dataiku, and IBM watsonx.data. Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=snowflake&focus%5B%5D=matlab&focus%5B%5D=hex-tech-hex&focus%5B%5D=dataiku&focus%5B%5D=ibm-watsonx-data) ## How Many Data Science and Machine Learning Platforms Products Does G2 Track? **Total Products under this Category:** 968 ### Category Stats (Jul 2026) - **Average Rating**: 4.46/5 (↑0.02 vs Jun 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: SutraAI (+14.29%) - Among all products in this category, SutraAI recorded the largest rating increase compared to last month *Last updated: July 17, 2026* ## How Does G2 Rank Data Science and Machine Learning Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 13,900+ Authentic Reviews - 968+ 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 Data Science and Machine Learning Platforms Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Cloudera 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. [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=692&secure%5Bchosen_at%5D=2026-07-18T11%3A55%3A48Z&secure%5Bdisplayable_resource_id%5D=692&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1886&secure%5Bresource_id%5D=692&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-science-and-machine-learning-platforms%3Fopen_modal_url%3D%252Fproducts%252Fanyscale%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fdata-science-and-machine-learning-platforms%2526source%253Dcategory&secure%5Btoken%5D=72ab679fae13829dc97bd6123c0e6ce68e997e762a24956d1fa2ae8d6c148f24&secure%5Burl%5D=https%3A%2F%2Fwww.cloudera.com%2Fproducts%2Fcloudera-data-platform%2Fcdp-demos.html%3Finternal_link%3Dp18%23get-started&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [Deci AI](https://www.g2.com/products/deci-ai/reviews) Advancements in AI, powered by deep learning, have triggered groundbreaking innovations. But, long development cycles, high compute costs, and poor inference performance are making it almost impossible for enterprises to productize AI. At Deci, we realized that the solution lay in harnessing the AI itself. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Deci AI?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) **Who Is the Company Behind Deci AI?** - **Seller:** [Deci AI](https://www.g2.com/sellers/deci-ai) - **Year Founded:** 2019 - **HQ Location:** Tel Aviv, IL - **LinkedIn® Page:** https://www.linkedin.com/company/deciai/?originalSubdomain=il (30 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Enterprise #### What Are Deci AI's Pros and Cons? **Pros:** - Ease of Use (1 reviews) - Easy Integrations (1 reviews) - Efficiency (1 reviews) - Fast Processing (1 reviews) - Innovation (1 reviews) **Cons:** - Complexity (1 reviews) - Difficult Setup (1 reviews) - Initial Difficulties (1 reviews) ### What Do G2 Reviewers Say About Deci AI? *AI-generated summary from verified user reviews* **Pros:** - Users find Deci AI to be **extremely easy to use** , facilitating seamless development and deployment of AI solutions. - Users praise the **easy integrations** of Deci AI, enabling seamless development and deployment of AI solutions. - Users value the **cost efficiency** of Deci AI, leading to enhanced performance without breaking the bank. - Users highlight the **fast processing** of Deci AI, making it easy to deploy and optimize AI solutions efficiently. - Users commend the **innovative technology** of Deci AI, enhancing performance and driving efficiency in their workflows. **Cons:** - Users find the **initial implementation complex** , suggesting a guided setup would greatly enhance their experience. - Users find the **difficult setup** of Deci AI challenging, especially those new to AI, indicating a need for guidance. - Users find the **initial implementation complex** , suggesting that a Guided Setup would greatly enhance user experience. #### What Are Recent G2 Reviews of Deci AI? **"["Deci AI: Pioneering AI Model Optimization for the Future"](https://www.g2.com/survey_responses/deci-ai-review-10669559)"** **Rating:** 4.0/5.0 stars *— Vinodh N.* [Read full review](https://www.g2.com/survey_responses/deci-ai-review-10669559) --- **"[Best AI platform to build and deploy AI Solutions](https://www.g2.com/survey_responses/deci-ai-review-10542102)"** **Rating:** 5.0/5.0 stars *— Shivam S.* [Read full review](https://www.g2.com/survey_responses/deci-ai-review-10542102) --- ### 2. [Deep.BI](https://www.g2.com/products/deep-bi/reviews) Deep.BI measures content consumption metrics and provides user engagement scoring to power publisher's content delivery, marketing tools and paywalls to grow, engage and retain audiences. Deep.BI collects all kinds of raw event data related to publishing, like reader’s behavior and content performance, and analyzes this data in real-time (sub-second latency between ingestion and data visualization). By collecting first-party raw data (no sampling & no aggregation), publishers get unprecedented flexibility in building their own metrics, reports, and different strategies for different kinds of content. This also allows publishers to quickly test hypotheses on both live and historical data. These dashboards and reports are shareable and customizable across teams making the workload on the analysts much lighter and gives them the ability to deliver what they want to deliver in the way they want and in lightning speeds! **Average Rating:** 4.4/5.0 **Total Reviews:** 10 **How Do G2 Users Rate Deep.BI?** - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep.BI?** - **Seller:** [Deep.BI](https://www.g2.com/sellers/deep-bi) - **Year Founded:** 2016 - **HQ Location:** San Francisco, California - **Twitter:** @_DeepBI (962 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deep-bi/ (18 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 40% Mid-Market #### What Are Deep.BI's Pros and Cons? **Pros:** - Analytics (2 reviews) - Insights (2 reviews) - Insights Generation (2 reviews) - Audience Engagement (1 reviews) - Automation (1 reviews) **Cons:** - Coding Difficulty (1 reviews) - Confusing Interface (1 reviews) - Not Intuitive (1 reviews) - Poor Interface Design (1 reviews) - Poor UI Design (1 reviews) ### What Do G2 Reviewers Say About Deep.BI? *AI-generated summary from verified user reviews* **Pros:** - Users value the **real-time actionable insights** from Deep.BI, enhancing their ability to analyze customer interactions effectively. - Users value the **real-time actionable insights** from Deep.BI, enhancing their ability to understand customer behavior swiftly. - Users value the **real-time actionable insights** from Deep.BI, enhancing data-driven decision-making for their clients. - Users value the **real-time actionable insights** from Deep.BI, enhancing audience engagement through informed decision-making. - Users value the **automation capabilities** of Deep.BI, streamlining analytics and enhancing dashboard creation for clients. **Cons:** - Users find the **coding difficulty** in Deep.BI to be a barrier, complicating their usage of the platform. - Users find the **confusing interface** of Deep.BI challenging, especially for those without technical expertise. - Users find the **UI/UX not intuitive** , making it difficult for layman users to navigate and utilize effectively. - Users find the **poor interface design** challenging for layman users, affecting overall usability and experience. - Users find the **UI design lacking** , making it difficult for non-technical individuals to navigate the platform effectively. #### What Are Recent G2 Reviews of Deep.BI? **"[Helpful real-time data processing](https://www.g2.com/survey_responses/deep-bi-review-10032368)"** **Rating:** 4.0/5.0 stars *— Frederic G.* [Read full review](https://www.g2.com/survey_responses/deep-bi-review-10032368) --- **"[Useful for processing data and assessing reports in real time](https://www.g2.com/survey_responses/deep-bi-review-10391757)"** **Rating:** 4.0/5.0 stars *— Komal R.* [Read full review](https://www.g2.com/survey_responses/deep-bi-review-10391757) --- #### What Are G2 Users Discussing About Deep.BI? - [What is the three 3 capabilities of BIS business intelligence system?](https://www.g2.com/discussions/what-is-the-three-3-capabilities-of-bis-business-intelligence-system) - [What are the functions of BI systems?](https://www.g2.com/discussions/what-are-the-functions-of-bi-systems) - [What are the key capabilities of BI?](https://www.g2.com/discussions/what-are-the-key-capabilities-of-bi) ### 3. [Deep Learning Containers](https://www.g2.com/products/deep-learning-containers/reviews) Google's Deep Learning Containers are pre-configured Docker images designed to streamline the development and deployment of deep learning models. These containers come equipped with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, along with their dependencies, enabling data scientists and developers to focus on model development without the hassle of environment setup. Key Features and Functionality: - Pre-configured Environments: Each container includes essential deep learning frameworks and libraries, ensuring compatibility and reducing setup time. - Scalability: Seamless integration with Google Cloud services allows for efficient scaling of training and inference tasks. - Flexibility: Support for various hardware accelerators, including GPUs and TPUs, enhances performance for computationally intensive tasks. - Portability: Consistent environments across development, testing, and production stages facilitate smoother transitions and deployments. Primary Value and Problem Solved: Deep Learning Containers address the complexities associated with setting up and managing deep learning environments. By providing ready-to-use, optimized containers, they eliminate the need for manual installation and configuration of machine learning frameworks and dependencies. This accelerates the development process, ensures consistency across different stages of model deployment, and allows teams to allocate more resources toward innovation and model refinement rather than infrastructure management. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Deep Learning Containers?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 6.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep Learning Containers?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,899,995 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Enterprise #### What Are Deep Learning Containers's Pros and Cons? **Pros:** - Easy Integrations (1 reviews) - Integrated Platform (1 reviews) **Cons:** - Complexity (1 reviews) ### What Do G2 Reviewers Say About Deep Learning Containers? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **easy integrations** offered by Deep Learning Containers, enhancing compatibility with various tools and services. - Users appreciate the **seamless integration** of Deep Learning Containers with Google Cloud Services, enhancing support for PyTorch and TensorFlow. **Cons:** - Users experience a **complex interface** that makes getting started with Deep Learning Containers challenging and overwhelming. #### What Are Recent G2 Reviews of Deep Learning Containers? **"[Deep Learning Containers use](https://www.g2.com/survey_responses/deep-learning-containers-review-10185255)"** **Rating:** 4.0/5.0 stars *— Verified User in Information Services* [Read full review](https://www.g2.com/survey_responses/deep-learning-containers-review-10185255) --- **"[Ready to use Docker Container for my ML Model](https://www.g2.com/survey_responses/deep-learning-containers-review-10262679)"** **Rating:** 5.0/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/deep-learning-containers-review-10262679) --- ### 4. [GGML](https://www.g2.com/products/ggml/reviews) GGML is a tensor library for machine learning, enabling complex models on regular hardware. **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate GGML?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind GGML?** - **Seller:** [GGML](https://www.g2.com/sellers/ggml) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business #### What Are Recent G2 Reviews of GGML? **"[Machine Learning AI](https://www.g2.com/survey_responses/ggml-review-10190670)"** **Rating:** 4.5/5.0 stars *— Vikram K.* [Read full review](https://www.g2.com/survey_responses/ggml-review-10190670) --- **"[Perform high-level machine learning operations on normal hardware](https://www.g2.com/survey_responses/ggml-review-10221848)"** **Rating:** 5.0/5.0 stars *— Hazel L.* [Read full review](https://www.g2.com/survey_responses/ggml-review-10221848) --- ### 5. [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?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 0.0/10 (Category avg: 8.2/10) **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% Mid-Market #### 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) ### 6. [Iguazio](https://www.g2.com/products/iguazio/reviews) Iguazio’s AI software enables enterprises to develop, deploy and manage AI applications, drastically shortening the time required to create real business value with AI. Using Iguazio, organizations can develop AI models at scale and in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals use Iguazio to solve the complexities of MLOps and create business impact through a multitude of ML and Generative AI use cases such as chatbot automation, fraud prediction, real-time recommendation engines and predictive maintenance. Iguazio was acquired by McKinsey & Company in January of 2023 and is now a part of QuantumBlack, McKinsey's AI arm. Iguazio brings data science to life. **Average Rating:** 4.8/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Iguazio?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 7.5/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Iguazio?** - **Seller:** [Iguazio](https://www.g2.com/sellers/iguazio) - **Year Founded:** 2014 - **HQ Location:** Herzliya, IL - **Twitter:** @iguazio (929 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/iguazio/ (74 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 25% Enterprise #### What Are Iguazio's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - AI Integration (1 reviews) - Automation (1 reviews) - Customization (1 reviews) - Deployment Ease (1 reviews) **Cons:** - Complexity (1 reviews) - Cost Transparency (1 reviews) - Lacking Features (1 reviews) - Lack of Guidance (1 reviews) - Learning Curve (1 reviews) ### What Do G2 Reviewers Say About Iguazio? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **production-ready AI capabilities** of Iguazio, streamlining the ML lifecycle for efficient model deployment. - Users appreciate the **seamless AI integration** of Iguazio, enhancing efficiency in the ML lifecycle and production readiness. - Users appreciate the **seamless automation** of Iguazio, enabling efficient transitions from development to production in AI projects. - Users appreciate the **customization capabilities** of Iguazio, enhancing flexibility and efficiency in managing AI workflows. - Users appreciate the **deployment ease** of Iguazio, enabling seamless transitions from development to production effortlessly. **Cons:** - Users find the **complexity** of Iguazio challenging for beginners, and the pricing lacks transparency. - Users find **cost transparency lacking** in Iguazio, making it challenging for beginners to understand pricing effectively. - Users find Iguazio **lacking features** like beginner-friendly support and clear pricing, impacting usability and transparency. - Users find Iguazio's **lack of guidance** challenging, especially beginners struggling with its complexity. - Users find Iguazio's **learning curve challenging** for beginners, creating hurdles in getting started effectively. #### What Are Recent G2 Reviews of Iguazio? **"[Iguazio Review: A Production-First MLOps Platform for Scalable AI Deployment](https://www.g2.com/survey_responses/iguazio-review-11148023)"** **Rating:** 4.5/5.0 stars *— Vaishali R.* [Read full review](https://www.g2.com/survey_responses/iguazio-review-11148023) --- **"[Powerful MLOpls platform and amazing support](https://www.g2.com/survey_responses/iguazio-review-9341928)"** **Rating:** 5.0/5.0 stars *— George L.* [Read full review](https://www.g2.com/survey_responses/iguazio-review-9341928) --- #### What Are G2 Users Discussing About Iguazio? - [What is Iguazio used for?](https://www.g2.com/discussions/what-is-iguazio-used-for) - 1 comment ### 7. [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 Admin:** 9.3/10 (Category avg: 8.5/10) **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) --- ### 8. [Kortical](https://www.g2.com/products/kortical/reviews) Kortical is an AI Cloud / ML platform that delivers acceleration without sacrificing any control over the model building process. Build AI in code or in the UI, depending on your preference. Key features are: - Powerful SDK and UI interfaces - Exploratory Data Analysis - Data Cleaning & Feature Engineering - Model Building With Interactive AutoML - Experiment Tracking - Explainability - "1 click" Deployment - Instant Apps to create Enterprise Grade ML apps in minutes (code included) The cloud infrastructure is set-up so all you need is a login and you can from raw data to fully deployed model in hours. Using Kortical's app templates Data scientists are empowered to own the process from raw data to fully deployed, enterprise grade application, based on industry leading stack, including Kubernetes, uwsgi and continuous integration. With Kortical you get the best of data scientist and machine and we have a team of data scientists on hand to support you at any stage of your ML solution delivery. Kortical gets more accurate model results than Google AutoML, Azure, Datarobot and more, do reach out to check it out. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Kortical?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Kortical?** - **Seller:** [Kortical](https://www.g2.com/sellers/kortical) - **Year Founded:** 2016 - **HQ Location:** London, GB - **Twitter:** @Kortical_ (352 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kortical/ (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market #### What Are Recent G2 Reviews of Kortical? **"[AI and machine learning platform that can democratise data science](https://www.g2.com/survey_responses/kortical-review-4207678)"** **Rating:** 5.0/5.0 stars *— Simon J.* [Read full review](https://www.g2.com/survey_responses/kortical-review-4207678) --- **"[A trusted machine-learning partner supercharged with Auto-ML that delivers high ROI](https://www.g2.com/survey_responses/kortical-review-4207912)"** **Rating:** 5.0/5.0 stars *— Abdelrahman Z.* [Read full review](https://www.g2.com/survey_responses/kortical-review-4207912) --- #### What Are G2 Users Discussing About Kortical? - [What is Kortical used for?](https://www.g2.com/discussions/what-is-kortical-used-for) ### 9. [Observable](https://www.g2.com/products/observable/reviews) Observable is a data analysis and exploration platform that helps data analysts and their stakeholders understand their business data, build charts and other visualizations, and communicate insights. Built for teams, powered by AI, and backed by a global community of data practitioners, Observable offers a single workspace for data exploration, custom chart creation, and cross-functional data collaboration. Observable's collaborative whiteboard for data analysis, Observable Canvases, helps analysts with exploratory data analysis, advanced chart creation, and data storytelling. It makes the data analysis process more streamlined by bringing querying, chart creation, and sharing all in one platform. Users can choose between code, UI, or AI, and easily move between all three depending on the task at hand. The AI is also deeply integrated and turnkey, operating transparently on the canvas to allow users to understand, interpret, and fine-tune its results and reasoning, building trust in its recommendations. Additionally, whiteboarding features like comments, illustrations, and annotations help bring stakeholders into the data analysis process to reduce frustrating back-and-forths. Sophisticated data visualizations such as beeswarms, Sankey diagrams, choropleths, and more are available out-of-the-box to enable users to easily create and share expressive, interactive charts. Users can embed charts in internal apps, and generate polished, stakeholder-friendly dashboards in just a few clicks. **Average Rating:** 4.4/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Observable?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Observable?** - **Seller:** [Observable](https://www.g2.com/sellers/observable) - **Year Founded:** 2017 - **HQ Location:** San Francisco, US - **Twitter:** @observablehq (25,314 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/observable/ (25 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 25% Mid-Market #### What Are Observable's Pros and Cons? **Pros:** - Charting Features (1 reviews) - Collaboration (1 reviews) - Data Visualization (1 reviews) - Design Aesthetics (1 reviews) - Ease of Use (1 reviews) **Cons:** - Data Management Issues (1 reviews) - Large Data Handling (1 reviews) - Slow Performance (1 reviews) ### What Do G2 Reviewers Say About Observable? *AI-generated summary from verified user reviews* **Pros:** - Users love the **seamless data exploration** and beautiful visuals offered by Observable's charting features. - Users love the **real-time collaboration** features of Observable, enhancing teamwork and data exploration effortlessly. - Users love the **effortless data visualization** capabilities of Observable, enabling seamless exploration and collaboration. - Users admire the **beautiful visual output** of Observable, enhancing their data storytelling and presentations. - Users praise the **ease of use** of Observable, allowing effortless coding and collaboration without setup hassles. **Cons:** - Users find Observable can feel **browser-heavy for large data sets** , impacting performance and user experience. - Users find Observable can feel **browser-heavy with large data sets** , impacting performance during heavy use. - Users find Observable **slow in performance** , especially with large data sets or complex applications, impacting usability. #### What Are Recent G2 Reviews of Observable? **"[A Game-Changer for Interactive Data Visualization](https://www.g2.com/survey_responses/observable-review-11720138)"** **Rating:** 5.0/5.0 stars *— Stephanie E.* [Read full review](https://www.g2.com/survey_responses/observable-review-11720138) --- **"[Best place to write interactive Data Visualization tools](https://www.g2.com/survey_responses/observable-review-8769653)"** **Rating:** 4.5/5.0 stars *— Anup J.* [Read full review](https://www.g2.com/survey_responses/observable-review-8769653) --- ### 10. [Obviously AI](https://www.g2.com/products/obviously-ai/reviews) Obviously AI is the fastest and easiest automated machine learning software that enables anyone to build predictive AI models in minutes, without writing code. All you do is connect your historical data, click a couple of buttons and your predictive AI models will be ready to use in just a matter of minutes. You can share these predictions with your team, simulate what-if scenarios and use our APIs to integrate with your everyday apps & services and automatically take action in real time. **Average Rating:** 4.0/5.0 **Total Reviews:** 4 **Who Is the Company Behind Obviously AI?** - **Seller:** [Obviously AI](https://www.g2.com/sellers/obviously-ai) - **Year Founded:** 2025 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/zamshq (24 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 50% Mid-Market #### What Are Recent G2 Reviews of Obviously AI? **"[Predict without code](https://www.g2.com/survey_responses/obviously-ai-review-7424828)"** **Rating:** 4.0/5.0 stars *— Akshay Kumar G.* [Read full review](https://www.g2.com/survey_responses/obviously-ai-review-7424828) --- **"[Some of the best data scientists in the space](https://www.g2.com/survey_responses/obviously-ai-review-8256708)"** **Rating:** 4.0/5.0 stars *— Nico G.* [Read full review](https://www.g2.com/survey_responses/obviously-ai-review-8256708) --- ### 11. [Olostep](https://www.g2.com/products/olostep/reviews) Built to power the Web's second user, Olostep (olostep.com) is the best web search, scraping and crawling API for AI. Olostep is powering the world's leading AI startups and agents. It transforms complex, JavaScript-heavy websites into clean, structured, LLM-ready data. The API returns formats like Markdown, JSON, HTML, PDF, and screenshots. Olostep is the most reliable and cost-effective solution on the market, suited for scalable business needs. Olostep is one of the few solutions on the market that does not strictly require a monthly subscription. You can buy one-time credits that are valid for 6 months. Get clean data for your AI from any website with Olostep. Test it for free. No credit card required. **Average Rating:** 4.3/5.0 **Total Reviews:** 2 **Who Is the Company Behind Olostep?** - **Seller:** [Olostep](https://www.g2.com/sellers/olostep) - **Year Founded:** 2024 - **HQ Location:** Dover, US - **LinkedIn® Page:** https://www.linkedin.com/company/olostep/ (5 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Olostep? **"[Effortless AI Data Extraction and Powerful /answer Research](https://www.g2.com/survey_responses/olostep-review-12823165)"** **Rating:** 4.0/5.0 stars *— Yasser S.* [Read full review](https://www.g2.com/survey_responses/olostep-review-12823165) --- **"[Powerful Parser Editor for Building Custom Parsers](https://www.g2.com/survey_responses/olostep-review-12808631)"** **Rating:** 4.5/5.0 stars *— Zawwar A.* [Read full review](https://www.g2.com/survey_responses/olostep-review-12808631) --- ### 12. [Plainsight](https://www.g2.com/products/plainsight/reviews) Plainsight is a Vision AI platform that helps enterprises turn images and video into actionable operational intelligence. The Plainsight Platform simplifies the full computer vision lifecycle, from data collection and model training to deployment, monitoring, and continuous improvement. With Plainsight, teams can build, manage, and scale custom computer vision solutions that improve visibility, automate manual processes, reduce risk, and uncover insights from real-world environments. Plainsight is designed for enterprises that need production-ready Vision AI across industries such as restaurants, retail, manufacturing, logistics, and other operationally complex environments.For more information, visit https://plainsight.ai. **Average Rating:** 4.5/5.0 **Total Reviews:** 5 **Who Is the Company Behind Plainsight?** - **Seller:** [Plainsight](https://www.g2.com/sellers/plainsight) - **Year Founded:** 2024 - **HQ Location:** Greater Seattle Area, US - **Twitter:** @PlainsightAI (1,456 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/plainsightai/ (22 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 80% Small-Business, 20% Mid-Market #### What Are Plainsight's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - AI Integration (1 reviews) - AI Modeling (1 reviews) - AI Technology (1 reviews) - Innovation (1 reviews) **Cons:** - Required Expertise (1 reviews) - Required Knowledge (1 reviews) ### What Do G2 Reviewers Say About Plainsight? *AI-generated summary from verified user reviews* **Pros:** - Users value the **no-code and low-code options** of Plainsight, enhancing accessibility to AI vision capabilities. - Users appreciate the **no-code and low-code options** , making AI vision more accessible to everyone. - Users value the **no-code and low-code options** , making AI vision more accessible for everyone. - Users find the **no-code and low-code options** of Plainsight make AI vision more accessible and user-friendly. - Users find the **no-code and low-code options** of Plainsight enhance accessibility to AI vision technology. **Cons:** - Users find the **required expertise** for advanced features challenging, even with no-code/low-code options available. - Users find that some **advanced features** of Plainsight require technical knowledge despite no-code options available. #### What Are Recent G2 Reviews of Plainsight? **"[Simplifying AI Vision with Powerful End-to-End Solutions](https://www.g2.com/survey_responses/plainsight-review-10956680)"** **Rating:** 4.5/5.0 stars *— Ajit S.* [Read full review](https://www.g2.com/survey_responses/plainsight-review-10956680) --- **"[An end-to-end innovative AI solution](https://www.g2.com/survey_responses/plainsight-review-9581873)"** **Rating:** 5.0/5.0 stars *— Nimish G.* [Read full review](https://www.g2.com/survey_responses/plainsight-review-9581873) --- #### What Are G2 Users Discussing About Plainsight? - [What is Plainsight used for?](https://www.g2.com/discussions/what-is-plainsight-used-for) ### 13. [Qualetics](https://www.g2.com/products/qualetics/reviews) Discover Qualetics, your gateway to effortless AI automation. Our cutting-edge platform is designed to provide businesses with a seamless experience with its intuitive no-code AI capabilities. Say goodbye to coding complexities and hello to the future of AI innovation! Key Features: No Code AI: Qualetics empowers you with a no-code AI platform, eliminating the need for programming expertise. Dive into the world of artificial intelligence effortlessly and bring your ideas to life without writing a single line of code. Pre-Trained AI Models: Benefit from over 25+ pre-trained AI models covering a diverse range of applications. Qualetics ensures that you have access to state-of-the-art models for tasks such as text analysis, image recognition, document processing, and audio/video data interpretation. Self Learning AI Models: Qualetics goes beyond static models. Our platform features self-learning AI models that adapt and evolve over time, ensuring that your solutions stay ahead of the curve and continuously improve their performance. Data Processing Capabilities: Seamlessly process text, image, document, audio, and video data with Qualetics. Our platform is your all-in-one solution for comprehensive data processing, enabling you to extract valuable insights from various types of content. Multi-Tenancy: Qualetics understands the importance of scalability and collaboration. Our multi-tenancy support allows multiple users or teams to work concurrently within the platform, ensuring efficiency and collaboration at every level. Security and Governance: Trust is paramount. Qualetics prioritizes your data's security with robust measures and governance protocols. Rest assured that your AI endeavors are protected and compliant with industry standards. Real-Time Observability: Stay informed and in control with real-time observability features. Monitor your AI models' performance, track usage metrics, and receive insights instantly. Qualetics gives you the tools to make informed decisions on the fly. Why Choose Qualetics: Simplicity Meets Innovation: Qualetics brings the power of AI to your fingertips without the complexity of coding. Diverse Model Library: Access a rich library of pre-trained models for a wide array of applications. Adaptive Learning: Benefit from self-learning AI models that adapt to evolving data patterns. Versatile Data Processing: Process text, image, document, audio, and video data seamlessly in one platform. Collaborative Environment: Foster collaboration with multi-tenancy support for teams of any size. Security-First Approach: Ensure the security and governance of your AI initiatives with our robust measures. Real-Time Insights: Make informed decisions with real-time observability features, putting you in control. Embark on your AI journey with Qualetics and experience the future of AI, simplified. Explore possibilities, innovate effortlessly! **Average Rating:** 4.4/5.0 **Total Reviews:** 5 **How Do G2 Users Rate Qualetics?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Qualetics?** - **Seller:** [Qualetics Data Machines](https://www.g2.com/sellers/qualetics-data-machines) - **Year Founded:** 2018 - **HQ Location:** Princeton, US - **LinkedIn® Page:** https://www.linkedin.com/company/qualetics (4 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 40% Mid-Market, 20% Enterprise #### What Are Qualetics's Pros and Cons? **Pros:** - Customer Insights (1 reviews) - Data Analytics (1 reviews) - Features (1 reviews) - Insights (1 reviews) - Personalization (1 reviews) **Cons:** - Dashboard Issues (1 reviews) - Limited Customization (1 reviews) - Limited Features (1 reviews) - Limited Flexibility (1 reviews) ### What Do G2 Reviewers Say About Qualetics? *AI-generated summary from verified user reviews* **Pros:** - Users value the **personalized experience** from Qualetics, enhancing customer loyalty and satisfaction through data-driven insights. - Users rave about the **personalized user experience** enabled by Qualetics, enhancing customer loyalty and satisfaction. - Users appreciate the **exceptional data-driven personalization** of Qualetics, enhancing user experience and boosting satisfaction. - Users value the **personalized user experience** provided by Qualetics, enhancing customer loyalty and satisfaction significantly. - Users appreciate the **fantastic personalization** of Qualetics, enhancing customer loyalty through tailored experiences and recommendations. **Cons:** - Users find the **limited customization options** of Qualetics insufficient for their specific business requirements and needs. - Users find the **limited customization options** of Qualetics inadequate for their specific business requirements. - Users find the **limited customization options** of Qualetics restrict their ability to tailor solutions to their unique needs. - Users find the **limited customization options** make it challenging to tailor dashboards and reports to unique business needs. #### What Are Recent G2 Reviews of Qualetics? **"[Streamlined Assessments with Minor Integration Hurdles](https://www.g2.com/survey_responses/qualetics-review-12971367)"** **Rating:** 4.0/5.0 stars *— Verified User in Mechanical or Industrial Engineering* [Read full review](https://www.g2.com/survey_responses/qualetics-review-12971367) --- **"[We Can Create Truly Personalized Experiences](https://www.g2.com/survey_responses/qualetics-review-11626254)"** **Rating:** 4.5/5.0 stars *— Pricilla M.* [Read full review](https://www.g2.com/survey_responses/qualetics-review-11626254) --- ### 14. [SutraAI](https://www.g2.com/products/sutraai/reviews) At Sutra.AI, we understand that you’re not just looking for AI technology; you’re looking for results. That’s why our end-to-end platform is designed to help you achieve your business goals through AI transformation. Unlike other AI platforms that bog you down with high costs and complexity, our patent-pending technology prioritizes outcomes, so you can start seeing results quickly! We work with you to identify the right AI projects for your business, and leverage our innovative AI platform to execute them successfully. **Average Rating:** 4.0/5.0 **Total Reviews:** 2 **Who Is the Company Behind SutraAI?** - **Seller:** [Sutra.AI](https://www.g2.com/sellers/sutra-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/sutra-ai/ (32 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of SutraAI? **"[User-Friendly, Transparent Automation with Responsive Support](https://www.g2.com/survey_responses/sutraai-review-12873213)"** **Rating:** 4.5/5.0 stars *— Verified User in Automotive* [Read full review](https://www.g2.com/survey_responses/sutraai-review-12873213) --- ### 15. [Tazi](https://www.g2.com/products/tazi/reviews) TAZI is an Adaptive Machine Learning platform serving business users. TAZI has been chosen as a Cool Vendor in Core AI Technologies by Gartner and is cited as a Responsible, Explainable AI vendor in various analyst reports. Also, Data Science Central considered TAZI as "The Next Generation of Auto ML”. TAZI platform is based on an architecture combining 30+ years of experience and 23 patents in AI. TAZI enables business experts (and data scientists) to easily create, update, deploy and take actions with ML. TAZI models are understandable and learn continuously from streaming data and humans. TAZI helps insurance, retail, pharma, health, finance, manufacturing, and telco industries in making smarter business decisions via democratizing AI. TAZI provides various solutions such as customer retention, churn prediction, sales forecasting, and demand prediction to many companies including Fortune 500 from its offices in San Francisco and Istanbul. We enable human and machine intelligence to work together! **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Tazi?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Tazi?** - **Seller:** [Tazi AI Systems](https://www.g2.com/sellers/tazi-ai-systems) - **Year Founded:** 2017 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/taziai/ (31 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Small-Business #### What Are Recent G2 Reviews of Tazi? **"[The perfect partner of my business!](https://www.g2.com/survey_responses/tazi-review-10175322)"** **Rating:** 4.5/5.0 stars *— Jacob H.* [Read full review](https://www.g2.com/survey_responses/tazi-review-10175322) --- **"[The Real Deal](https://www.g2.com/survey_responses/tazi-review-10647212)"** **Rating:** 5.0/5.0 stars *— Catherine M.* [Read full review](https://www.g2.com/survey_responses/tazi-review-10647212) --- ### 16. [The Cognite AI & Data Platform](https://www.g2.com/products/the-cognite-ai-data-platform/reviews) The Cognite AI and Data Platform™ is a sophisticated Industrial DataOps solution specifically designed for asset-intensive industries seeking to harness the power of their operational and engineering data. Founded in 2016 and based in Tempe, Arizona, Cognite aims to facilitate the transformation of complex data environments into actionable insights that drive efficiency and innovation across various sectors. This cloud-native platform excels in ingesting and contextualizing data from a multitude of sources, including Information Technology (IT), Operational Technology (OT), and engineering systems. By creating a unified industrial knowledge graph, the Cognite AI and Data Platform integrates data from historians, Enterprise Resource Planning (ERP) systems, Computerized Maintenance Management Systems (CMMS), and even 3D models. This comprehensive approach allows organizations to standardize their data models and utilize robust APIs, enabling secure workspaces that support advanced analytics, interactive dashboards, and AI-driven applications. Targeted primarily at industries that rely heavily on operational data, such as manufacturing, energy, and utilities, the Cognite AI and Data Platform addresses specific use cases that enhance productivity and operational efficiency. For instance, organizations can leverage the platform for production optimization, where real-time data insights lead to improved throughput and reduced operational bottlenecks. Additionally, the platform supports predictive maintenance initiatives, allowing companies to anticipate equipment failures before they occur, thereby minimizing downtime and associated costs. Key features of the Cognite AI and Data Platform include its ability to transform fragmented data into a trusted and contextual foundation, which is crucial for making informed decisions. By providing a centralized repository of data, users gain full ownership and control over their information, facilitating compliance and security. Moreover, the platform’s scalability enables organizations to implement AI initiatives that can evolve with their operational needs, ensuring that they remain competitive in a rapidly changing industrial landscape. Overall, the Cognite AI and Data Platform stands out in the DataOps category by offering a comprehensive solution that not only integrates disparate data sources but also empowers organizations to unlock the full potential of their industrial data. Through its focus on contextualization and user-friendly interfaces, it provides significant value to companies looking to enhance their operational capabilities and drive long-term growth. **Average Rating:** 4.8/5.0 **Total Reviews:** 3 **Who Is the Company Behind The Cognite AI & Data Platform?** - **Seller:** [Cognite](https://www.g2.com/sellers/cognite) - **Company Website:** https://www.cognite.com/en/ - **Year Founded:** 2016 - **HQ Location:** Tempe, Arizona, United States - **LinkedIn® Page:** https://www.linkedin.com/company/cognitedata (760 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are Recent G2 Reviews of The Cognite AI & Data Platform? **"[Empowers Data Contextualization with Ease and Trust](https://www.g2.com/survey_responses/the-cognite-ai-data-platform-review-12948927)"** **Rating:** 5.0/5.0 stars *— Jeremy K.* [Read full review](https://www.g2.com/survey_responses/the-cognite-ai-data-platform-review-12948927) --- **"[Connects Nearly Any Industrial Data Source into a Common Source of Truth](https://www.g2.com/survey_responses/the-cognite-ai-data-platform-review-12951453)"** **Rating:** 5.0/5.0 stars *— Adam G.* [Read full review](https://www.g2.com/survey_responses/the-cognite-ai-data-platform-review-12951453) --- ### 17. [Trendskout](https://www.g2.com/products/trendskout/reviews) Automate your Business with ready to use Machine Learning **Average Rating:** 3.0/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Trendskout?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Trendskout?** - **Seller:** [Trendskout](https://www.g2.com/sellers/trendskout) - **Year Founded:** 2019 - **HQ Location:** Ghent, BE - **LinkedIn® Page:** https://www.linkedin.com/company/trendskout (17 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 18. [Ultralytics](https://www.g2.com/products/ultralytics/reviews) Ultralytics is a prominent player in the field of vision AI, specializing in advanced computer vision solutions through its innovative YOLO (You Only Look Once) models. Designed to assist users in various industries, Ultralytics' technology enables real-time object detection and image analysis, making it an essential tool for businesses looking to leverage artificial intelligence for enhanced operational efficiency and decision-making. Targeted at a diverse audience that includes professionals in manufacturing, healthcare, transportation, agriculture, and retail, Ultralytics' offerings cater to organizations seeking to implement AI-driven solutions. The versatility of the YOLO models allows users to address a wide range of use cases, from automating quality control in manufacturing to improving patient outcomes in healthcare settings. By providing accessible and efficient AI tools, Ultralytics empowers businesses to harness the power of computer vision, ultimately driving innovation and growth. Key features of Ultralytics' technology include its remarkable speed and accuracy in image processing, which allows for the analysis of 1.6 billion images daily. This capability is complemented by the ability to train 5 million models per day, ensuring that users have access to the most up-to-date and effective AI tools. The YOLO models are designed to be user-friendly, enabling users with varying levels of technical expertise to implement and benefit from the technology without extensive training or resources. The unique selling points of Ultralytics lie in its commitment to AI accessibility and efficiency. By providing open-source solutions with extensive community support, the company fosters collaboration and innovation within the AI space. The impressive track record of over 110,000 GitHub stars and more than 100 million downloads highlights the widespread adoption and trust in Ultralytics' models. As industries continue to evolve and embrace digital transformation, Ultralytics remains at the forefront, offering cutting-edge solutions that meet the demands of a rapidly changing technological landscape. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Ultralytics?** - **Natural Language Understanding:** 3.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Ultralytics?** - **Seller:** [Ultralytics](https://www.g2.com/sellers/ultralytics) - **Company Website:** https://ultralytics.com - **Year Founded:** 2022 - **HQ Location:** 5001 Judicial Way Frederick, MD 21703, USA - **Twitter:** @ultralytics (8,876 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ultralytics (37 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Ultralytics's Pros and Cons? **Pros:** - Deployment Ease (2 reviews) - Ease of Use (2 reviews) - Efficiency (2 reviews) - AI Technology (1 reviews) - Automation (1 reviews) **Cons:** - Poor Documentation (2 reviews) - AI Limitations (1 reviews) - Confusing Documentation (1 reviews) - Deployment Issues (1 reviews) - Insufficient Learning Resources (1 reviews) ### What Do G2 Reviewers Say About Ultralytics? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **deployment ease** of Ultralytics, enabling quick and efficient adaptations for various applications. - Users value the **ease of use** in Ultralytics, as it allows for quick deployment and customization. - Users value the **efficiency** of Ultralytics for rapid deployment and customization on edge devices. - Users value the **efficient deployment** capabilities of Ultralytics, enabling optimized models for edge devices like Jetson ORIN. - Users benefit from **efficient automation** for training and deploying models on edge devices like Jetson ORIN. **Cons:** - Users find the **documentation lacking** in detail and timeliness, leading to confusion and misunderstandings. - Users find the **documentation lacking for advanced deployment scenarios** , making certain tasks more complex than necessary. - Users find the **documentation confusing** due to outdated information and AI-generated responses leading to misunderstandings. - Users face challenges with **deployment issues** , citing inadequate documentation for advanced scenarios and codec support problems. - Users often find **insufficient learning resources** , with outdated documentation and unclear initial responses causing confusion. #### What Are Recent G2 Reviews of Ultralytics? **"[Easy - Fast - Very good results at first try](https://www.g2.com/survey_responses/ultralytics-review-11773857)"** **Rating:** 5.0/5.0 stars *— Verified User in Logistics and Supply Chain* [Read full review](https://www.g2.com/survey_responses/ultralytics-review-11773857) --- **"[Edge devices support is Incredible](https://www.g2.com/survey_responses/ultralytics-review-11773759)"** **Rating:** 5.0/5.0 stars *— Sahil P.* [Read full review](https://www.g2.com/survey_responses/ultralytics-review-11773759) --- ### 19. [Wallaroo.ai](https://www.g2.com/products/wallaroo-ai/reviews) Easy Production AI at Scale: Any Model, Any Hardware, Anywhere. Purpose built for production AI, so AI teams stay lean and nimble. Enabling you to get to value fast for your cloud analytics, edge AI and gen AI initiatives. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Wallaroo.ai?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Wallaroo.ai?** - **Seller:** [Wallaroo](https://www.g2.com/sellers/wallaroo) - **Year Founded:** 2017 - **HQ Location:** New York, US - **Twitter:** @Wallarooai (736 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/wallarooai (44 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Wallaroo.ai? **"[Elevates Machine Learning Deployment and Monitoring](https://www.g2.com/survey_responses/wallaroo-ai-review-12679117)"** **Rating:** 4.5/5.0 stars *— Shahid A.* [Read full review](https://www.g2.com/survey_responses/wallaroo-ai-review-12679117) --- **"[Good tool](https://www.g2.com/survey_responses/wallaroo-ai-review-9509056)"** **Rating:** 4.5/5.0 stars *— Ankit M.* [Read full review](https://www.g2.com/survey_responses/wallaroo-ai-review-9509056) --- ### 20. [Zepl](https://www.g2.com/products/zepl/reviews) Zepl let you use data science to analyze your cloud data warehouse in minutes. Customers use Zepl for all kinds of use cases, including predictive analytics, marketing analytics, preventive maintenance, security, anomaly detection, sales forecasting, product recommendations and more. Zepl is an extensible, cloud-based data science and analytics platform for enterprise teams. With Zepl, teams of data analysts and data scientists can use Python, R, Spark, Scala, and SQL to find insights and make predictions about their most important business challenges, as well as package and present their findings using built-in advanced visualizations. **Average Rating:** 4.4/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Zepl?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Zepl?** - **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot) - **Year Founded:** 2012 - **HQ Location:** Boston, Massachusetts - **Twitter:** @DataRobot (19,225 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (883 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business #### What Are Recent G2 Reviews of Zepl? **"[Review_Zepl](https://www.g2.com/survey_responses/zepl-review-8566779)"** **Rating:** 5.0/5.0 stars *— Suraj T.* [Read full review](https://www.g2.com/survey_responses/zepl-review-8566779) --- **"[Versatile language good productivity](https://www.g2.com/survey_responses/zepl-review-9830124)"** **Rating:** 4.0/5.0 stars *— Dilip S.* [Read full review](https://www.g2.com/survey_responses/zepl-review-9830124) --- ### 21. [Afinsys](https://www.g2.com/products/afinsys/reviews) Our platform generates and repairs scraping code, adapting to website changes on the fly. With our no-code, easy-to-use interface, companies can scale their web data extraction efforts without the tedious task of building scraping bots for each individual website. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Afinsys?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Afinsys?** - **Seller:** [Affinsys](https://www.g2.com/sellers/affinsys) - **Year Founded:** 2017 - **HQ Location:** Dubai, AE - **LinkedIn® Page:** https://www.linkedin.com/company/affinsys-ai/ (73 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Afinsys? **"[Affinsys: Easy AI Solutions for Better Customer Experience](https://www.g2.com/survey_responses/afinsys-review-10251568)"** **Rating:** 5.0/5.0 stars *— Aman K.* [Read full review](https://www.g2.com/survey_responses/afinsys-review-10251568) --- ### 22. [Altair Knowledge Studio](https://www.g2.com/products/altair-knowledge-studio/reviews) Advanced Machine Learning and Artificial Intelligence Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and Explainable AI without restricting how models are configured and tuned, giving you control over model building. Key Features: - No Code Machine Learning Modeling - Transparent, Explainable AI - Predictive to Prescriptive Analytics **Average Rating:** 4.0/5.0 **Total Reviews:** 5 **How Do G2 Users Rate Altair Knowledge Studio?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) **Who Is the Company Behind Altair Knowledge Studio?** - **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7) - **Year Founded:** 1985 - **HQ Location:** Troy, MI - **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (2,774 employees on LinkedIn®) - **Ownership:** NASDAQ:ALTR - **Total Revenue (USD mm):** $458 **Who Uses This Product?** - **Company Size:** 80% Enterprise, 20% Small-Business #### What Are Recent G2 Reviews of Altair Knowledge Studio? **"[Excellent Tool for Fast Analytics](https://www.g2.com/survey_responses/altair-knowledge-studio-review-1765262)"** **Rating:** 5.0/5.0 stars *— Verified User in Financial Services* [Read full review](https://www.g2.com/survey_responses/altair-knowledge-studio-review-1765262) --- **"[The best data mining tool](https://www.g2.com/survey_responses/altair-knowledge-studio-review-3575348)"** **Rating:** 5.0/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/altair-knowledge-studio-review-3575348) --- #### What Are G2 Users Discussing About Altair Knowledge Studio? - [What is Altair Knowledge Studio used for?](https://www.g2.com/discussions/what-is-altair-knowledge-studio-used-for) ### 23. [Apache Zeppelin packaged by Data Science Dojo](https://www.g2.com/products/apache-zeppelin-packaged-by-data-science-dojo/reviews) Apache Zeppelin is an open-source tool that equips you with a web-based notebook that can be used for machine learning operations, interactive data analytics, visualization, and exploration. Vibrant designs and pictures generated can save time for users in the identification of key trends in data and ultimately accelerates the decision-making processes. This offer not only contains different pre-installed interpreters but also allows you to plug in your own various language backends for desirability. Apache Zeppelin supports many data sources which allow you to synthesize your data to visualize into interactive plots and charts. You can also create dynamic forms in your notebook and can share your notebook with collaborators. Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Apache Zeppelin packaged by Data Science Dojo?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Apache Zeppelin packaged by Data Science Dojo?** - **Seller:** [Data Science Dojo](https://www.g2.com/sellers/data-science-dojo) - **HQ Location:** Redmond, US - **Twitter:** @DataScienceDojo (227,307 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/data-science-dojo (185 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 24. [Artivatic](https://www.g2.com/products/artivatic/reviews) Artivatic empowers insurance, broker & re-insurance businesses and developers to re-imagine insurance products for the next billion users. Artivatic builds low-cost, modular API infrastructure so that you can go live in a matter of days, not months. The Artivatic is developing its propertioery cutting edge solutions to enable enterprises for 1 Billion people to get access to insurance, financial and health benefits with alternative data sources to increase their productivity, efficiency, automation power, and profitability, hence improving their way of doing business more intelligently seamlessly. Artivatic offers insurance underwriting, distribution, sales, agent efficiency, enabling branches /offices to go digital, fraud, prediction, personalization, recommendation, risk profiling, consumer profiling intelligence, KYC Automation & Compliance, automated decisions, monitoring, claims processing, sentiment/psychology behaviour, auto insurance claims, travel insurance, disease prediction, device based health profiling, wellness, APIs, and more. It enables businesses to have in-depth multi source data focused intelligence & decisions. Our Pilots have demonstrated more than 50% of efficiency, 90% reduction in turnaround time, 80% better fraud identification, 70% less cost reduction, more than 60% consumer engagement, more than 40% better risk delinquencies identification and intelligence in near real time. Call us at 08041502526 for more info or write to layak@artivatic.ai **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Artivatic?** - **Seller:** [Artivatic.ai](https://www.g2.com/sellers/artivatic-ai) - **Year Founded:** 2018 - **HQ Location:** Gurugram, IN - **LinkedIn® Page:** https://www.linkedin.com/company/artivatic (107 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are Recent G2 Reviews of Artivatic? **"[Good analystics platform](https://www.g2.com/survey_responses/artivatic-review-668969)"** **Rating:** 5.0/5.0 stars *— Verified User in Retail* [Read full review](https://www.g2.com/survey_responses/artivatic-review-668969) --- #### What Are G2 Users Discussing About Artivatic? - [What is Artivatic used for?](https://www.g2.com/discussions/what-is-artivatic-used-for) ### 25. [Braintrust Data](https://www.g2.com/products/braintrust-data/reviews) Braintrust Data is a comprehensive data management platform designed to empower organizations by transforming raw data into actionable insights. It offers a suite of tools that facilitate data integration, analysis, and visualization, enabling businesses to make informed decisions efficiently. Key Features and Functionality: - Data Integration: Seamlessly combines data from multiple sources, ensuring a unified and consistent dataset. - Advanced Analytics: Utilizes sophisticated algorithms to uncover patterns, trends, and correlations within the data. - Customizable Dashboards: Provides interactive dashboards that can be tailored to specific business needs, offering real-time insights. - Scalability: Designed to handle large volumes of data, accommodating the growth of an organization. - Security: Implements robust security measures to protect sensitive information and ensure compliance with data regulations. Primary Value and Solutions: Braintrust Data addresses the challenge of managing and interpreting vast amounts of data by offering a streamlined platform that simplifies data processes. It enables organizations to harness the full potential of their data, leading to improved operational efficiency, strategic planning, and competitive advantage. By providing tools for integration, analysis, and visualization, Braintrust Data ensures that businesses can make data-driven decisions with confidence. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Braintrust Data?** - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 3.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Braintrust Data?** - **Seller:** [Braintrust](https://www.g2.com/sellers/braintrust-70da938f-eb27-4a47-ab01-a0bb5c7c9102) - **Year Founded:** 2023 - **HQ Location:** San Francisco, California, United States - **LinkedIn® Page:** https://www.linkedin.com/company/braintrust-data (53 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are Recent G2 Reviews of Braintrust Data? **"[Braintrust: Meaningful AI Work with Skilled Contributors](https://www.g2.com/survey_responses/braintrust-data-review-13092111)"** **Rating:** 5.0/5.0 stars *— Rahul S.* [Read full review](https://www.g2.com/survey_responses/braintrust-data-review-13092111) --- ## What Is Data Science and Machine Learning Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to Data Science and Machine Learning Platforms? - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) --- ## How Do You Choose the Right Data Science and Machine Learning Platforms? ### What You Should Know About Data Science and Machine Learning Platforms ### What are data science and machine learning (DSML) platforms? 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 data science, of which [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning. ### Types of DSML platforms Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy [machine learning models](https://www.g2.com/articles/what-is-machine-learning). However, they differ in terms of the data types supported and the method and manner of deployment.  **Cloud** **data science and machine learning platforms** With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both 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 that have been deployed. **On-premises** **data science and machine learning platforms** 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 several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as [HIPAA](https://www.g2.com/glossary/hipaa-definition), require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. **Edge** **platforms** Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of [data centers](https://www.g2.com/glossary/data-center-definition) that process and store data locally before being sent to a centralized storage center or cloud. [Edge computing](https://learn.g2.com/trends/edge-computing) optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** ** ### What are the common features of data science and machine learning solutions? The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. **Data preparation:** Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for [data cleansing](https://www.g2.com/articles/data-cleaning) and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. **Model training:** Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are [supervised learning and unsupervised learning](https://www.g2.com/articles/supervised-vs-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 that 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. **Model deployment:** The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more. ### What are the benefits of using DSML engineering platforms? Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them 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 can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork. **Simplify and scale data science:** Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets. **Experimentation:** 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. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for [deep learning](https://www.g2.com/articles/deep-learning), which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation. ### Who uses data science and machine learning products? Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include 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 them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that 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:** 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 data science and machine learning platforms to bring AI into their organizations. **Professional data scientists:** Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and 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 data science and machine learning platforms? Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely: [AI & machine learning operationalization software](https://www.g2.com/categories/ai-machine-learning-operationalization) **:** Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models. [Machine learning software](https://www.g2.com/categories/machine-learning) **:** Data science and machine learning platforms are great for the full-scale development of models, whether that be for [computer vision](https://learn.g2.com/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. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, [Bayesian networks](https://www.g2.com/articles/artificial-intelligence-terms#:~:text=Bayesian%20network%3A%20also%20known%20as%20the%20Bayes%20network%2C%20Bayes%20model%2C%20belief%20network%2C%20and%20decision%20network%2C%20is%20a%20graph%2Dbased%20model%20representing%20a%20set%20of%20variables%20and%20their%20dependencies.%C2%A0), clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions. ### **Software and services related to data science and machine learning engineering platforms** Related solutions that can be used together with DSML 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 data science and machine learning 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 many 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, which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data ingested by data science and machine learning platforms. [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](https://www.g2.com/articles/natural-language-processing) 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](https://www.g2.com/articles/voice-recognition) and [natural language generation (NLG)](https://www.g2.com/categories/natural-language-generation-nlg), which converts data into understandable human language. Some examples of NLP uses include [chatbots](https://www.g2.com/categories/chatbots), translation applications, and [social media monitoring tools](https://www.g2.com/categories/social-media-listening-tools) that scan social media networks for mentions. ### Challenges with DSML platforms Software solutions can come with their own set of challenges.  **Data requirements:** A great deal of data is required for most AI algorithms to learn what is needed. 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 necessary actions. 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 by 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 DSML engineering 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:** AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to 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 choose the best data science and machine learning (DSML) platform #### Requirements gathering (RFI/RFP) for DSML platforms If a company is just starting out and looking to purchase its 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, it needs to 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 deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful. #### Compare DSML products **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 a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition. #### Selection of DSML platforms **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, 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 to recommend 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. ### Cost of data science and machine learning platforms As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs.  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 frequently not have as many features and may have usage caps. DSML 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 data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is 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 data science and machine learning platforms **How are DSML software tools 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 that be 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 DSML platform implementation?** It may require many people or 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, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management. **What is the implementation process for data science and machine learning products?** In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better 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 is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and 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. **When should you implement DSML tools?** As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, 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.  ### Data science and machine learning 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 is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it. Using embedded AI inside software like [CRM](https://www.g2.com/categories/crm), [marketing automation](https://www.g2.com/categories/marketing-automation), and [analytics solutions](https://www.g2.com/categories/analytics-tools-software) allows us 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 same way cloud deployment and mobile capabilities have over the past decade. 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 provide [MLaaS](https://www.g2.com/articles/machine-learning-as-a-service) for other enterprises. Developers quickly take advantage of these prebuilt algorithms and solutions by feeding them their 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 companies continue to rely on these microservices and the need for AI increases. **Explainability** When it comes to machine learning algorithms, especially deep learning, it may be 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. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law and the GDPR. --- ## What Are the Most Common Questions About Data Science and Machine Learning Platforms? *AI-generated · Last updated: April 27, 2026* ### Leading machine learning services for enterprise Based on G2 reviews, enterprise teams often favor platforms that unify data preparation, model training, deployment, governance, and monitoring in one environment. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — unified ML lifecycle and deployment. - [Databricks](https://www.g2.com/products/databricks/reviews) — lakehouse workflows with collaborative notebooks. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — large-scale analytics with governance. - [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) — governed AI development for enterprises. ### Top-rated software for data analysis in SaaS industry Based on G2 reviews, buyers in software environments often prioritize platforms that shorten analysis cycles, support collaboration, and reduce tool switching. - [Hex](https://www.g2.com/products/hex-tech-hex/reviews) — SQL, Python, and dashboarding together. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — end-to-end ML workflows in one place. - [Databricks](https://www.g2.com/products/databricks/reviews) — scalable analytics and ML collaboration. - [Deepnote](https://www.g2.com/products/deepnote/reviews) — collaborative notebooks for team analysis. ### Which platform offers the best machine learning solutions Based on G2 reviews, the strongest options depend on whether your team values unified workflows, low-code model building, notebook collaboration, or governance. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — managed training, deployment, and monitoring. - [Databricks](https://www.g2.com/products/databricks/reviews) — engineering, analytics, and ML together. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — advanced analytics with strong controls. - [Anaconda Platform](https://www.g2.com/products/anaconda-platform/reviews) — reproducible environments and package management. ### What are data science and machine learning platforms used for According to verified users, data science and machine learning platforms are used to centralize the work of preparing data, building models, testing ideas, deploying models, and sharing results. Reviews repeatedly mention workflow simplification as a major benefit: teams can reduce tool switching, automate repetitive preparation tasks, and move from experimentation to production with less manual setup. Buyers also use these platforms for dashboards, forecasting, predictive modeling, model monitoring, collaboration across technical and non-technical teams, and connecting data from warehouses, cloud systems, spreadsheets, or operational tools. Common buyer concerns in the reviews include learning curve, documentation quality, cost visibility, and performance on very large workloads. ### How do teams use data science and machine learning platforms for collaboration According to verified users, collaboration is one of the most practical reasons teams adopt these platforms. Reviews describe analysts, data scientists, and engineers working in shared notebooks, common environments, and governed workspaces so they can move from raw data to analysis, visualizations, and deployed models without passing files back and forth. Teams also mention easier sharing of dashboards, published apps, reusable workflows, and reproducible environments. In several reviews, this reduces friction between technical and non-technical stakeholders because results can be reviewed, discussed, and reused in one place. The strongest collaboration themes in the recent reviews are shared notebooks, consistent environments, versioned workflows, and easier handoffs into production.