# Best Data Science and Machine Learning Platforms - Page 6 *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 (1,316 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 (769 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 (210 reviews) | End-to-end ML workflows with no-code/code flexibility | "[Intuitive and Powerful for Machine Learning Experiments](https://www.g2.com/survey_responses/dataiku-review-13117166)" | | 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 | "[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)" | | 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 | [Anaconda Core](https://www.g2.com/products/anaconda-core/reviews) | 4.5/5.0 (235 reviews) | Dependency-free Python environment setup for data science | "[All-in-One Toolkit for Data Science Workflows](https://www.g2.com/survey_responses/anaconda-core-review-12706297)" | ## 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=142374&focus%5B%5D=7150) Highlighted products: Databricks, Gemini Enterprise Agent Platform, SAS Viya, Snowflake, MATLAB, Hex, Deepnote, and Dataiku. 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=deepnote&focus%5B%5D=dataiku) ## 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** ### SAS Viya SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control. [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=2026-07-17T11%3A34%3A41Z&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=1327283&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%252Fapache-zeppelin-packaged-by-data-science-dojo%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fdata-science-and-machine-learning-platforms%2526source%253Dcategory&secure%5Btoken%5D=ace64c02d88d15f3bf11899dd0357e7c74fb77c215aebf98593574a588076708&secure%5Burl%5D=https%3A%2F%2Fwww.sas.com%2Fgms%2Fredirect.jsp%3Fdetail%3DPLN73455_275629423&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [C3 AI Suite](https://www.g2.com/products/c3-ai-suite/reviews) C3 Ex Machina allows you to fuse together and visually explore data across multiple datasets to build smart customer segments, predict asset failure, and understand your future business needs. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate C3 AI Suite?** - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind C3 AI Suite?** - **Seller:** [C3.ai](https://www.g2.com/sellers/c3-ai) - **Year Founded:** 2009 - **HQ Location:** Redwood City, CA - **Twitter:** @C3IoT (76 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/c3-ai/ (1,140 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of C3 AI Suite? **"[Best suitable for the current world and as latest technologies](https://www.g2.com/survey_responses/c3-ai-suite-review-3581535)"** **Rating:** 4.5/5.0 stars *— sameer b.* [Read full review](https://www.g2.com/survey_responses/c3-ai-suite-review-3581535) --- #### What Are G2 Users Discussing About C3 AI Suite? - [What is C3 AI Suite used for?](https://www.g2.com/discussions/c3-ai-suite-what-is-c3-ai-suite-used-for) - [What is C3 AI Suite used for?](https://www.g2.com/discussions/what-is-c3-ai-suite-used-for) ### 2. [Chooch](https://www.g2.com/products/chooch/reviews) Chooch makes cameras intelligent. The Chooch AI Vision platform combines the power of Generative AI and Computer Vision technology to help businesses gain real-time actionable insights from their video and visual data. Chooch's AI technology instantly detects specific visuals, objects, and actions in videos and images, including critical anomalies and instantly comprehends their significance, sending real-time alerts to business systems to initiate further action. It does this in a fraction of the time a human being could. Businesses use Chooch to automate repetitive, manual video review tasks, making searching video data more efficient and allowing businesses to reallocate human resources to higher value activities. Chooch is being used across many different applications including detecting retail theft, monitoring workplace safety, detecting weapons, monitoring self-check out, digital asset management, and more. **Average Rating:** 4.0/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Chooch?** - **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:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Chooch?** - **Seller:** [Chooch](https://www.g2.com/sellers/chooch) - **Year Founded:** 2015 - **HQ Location:** San Mateo, California - **Twitter:** @Chooch_AI (1,714 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/chooch/ (50 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Mid-Market #### What Are Recent G2 Reviews of Chooch? **"[Chooch is a gamechanger in its field.](https://www.g2.com/survey_responses/chooch-review-8752556)"** **Rating:** 4.0/5.0 stars *— Pratim G.* [Read full review](https://www.g2.com/survey_responses/chooch-review-8752556) --- **"[Driving revolution through AI Engine](https://www.g2.com/survey_responses/chooch-review-9516689)"** **Rating:** 4.0/5.0 stars *— Aman A.* [Read full review](https://www.g2.com/survey_responses/chooch-review-9516689) --- ### 3. [CoPilot-AI](https://www.g2.com/products/copilot-ai-copilot-ai/reviews) CoPilot-AI is an advanced tactical decision-support system designed to enhance wildfire management through the integration of cutting-edge Artificial Intelligence technologies. Developed by an interdisciplinary team at Maynooth University, CoPilot-AI leverages drones, robotics, sensors, and machine learning to construct a Common Operational Picture (COP) digital platform. This platform facilitates smart multi-thematic sensor data capture, automated data fusion, deep learning analysis, and effective information sharing, thereby enabling more informed and timely decision-making during wildfire events. Key Features and Functionality: - AI-Driven Data Analysis: Utilizes machine learning algorithms to process and interpret complex data sets, providing actionable insights for wildfire management. - Drone and Robotic Sensing Platforms: Employs advanced drones and robotic systems for real-time monitoring and data collection in wildfire-affected areas. - Automated Data Fusion: Integrates data from various sensors and sources to create a comprehensive and cohesive operational picture. - Dynamic Terrain and Scene Analysis: Analyzes changing environmental conditions to assess risk and predict wildfire behavior. - Multi-Criteria Risk Modeling: Evaluates multiple factors to model and predict potential wildfire risks, aiding in proactive management strategies. Primary Value and Problem Solved: CoPilot-AI addresses the critical need for improved wildfire response and management by providing a unified platform that enhances situational awareness and decision-making capabilities. By integrating AI technologies with real-time data collection and analysis, it enables emergency response teams to make informed decisions swiftly, reducing the threat to human life and minimizing economic and environmental damage caused by wildfires. This system streamlines coordination among various agencies and organizations involved in wildfire response, ensuring a more efficient and effective approach to managing these natural disasters. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind CoPilot-AI?** - **Seller:** [CoPilot-AI](https://www.g2.com/sellers/copilot-ai-bb04b437-7b24-47ca-b423-70df19003b15) - **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:** 100% Small-Business #### What Are Recent G2 Reviews of CoPilot-AI? **"[Reliable Copilot With Valuable, Well-Sourced Information](https://www.g2.com/survey_responses/copilot-ai-review-12703056)"** **Rating:** 5.0/5.0 stars *— Verified User in Hospital & Health Care* [Read full review](https://www.g2.com/survey_responses/copilot-ai-review-12703056) --- ### 4. [Data Science Wizards](https://www.g2.com/products/data-science-wizards/reviews) Data Science Wizards (DSW) is building UnifyAI OS - the Enterprise AI Operating System (AI OS) for regulated and hybrid enterprises. As AI moves from experimentation into execution-especially with the rise of agentic systems- enterprises face a fundamental question: How do we build, deploy, operate, and govern AI continuously, safely, and at scale as part of the enterprise itself? DSW Enterprise AI OS enables enterprises to: • Build, integrate, deploy, orchestrate, govern, monitor, and continuously operate AI/ML and agentic workloads • Treat AI use cases, AI workloads, and AI applications as long-running production systems • Enforce governance-as-code at runtime, not through static policy documents • Retain full ownership of all AI artifacts and source code built by the enterprise • Run AI entirely inside customer-controlled environments (on-premises, local, private cloud, hybrid) • Integrate a broad AI ecosystem without vendor lock-in DSW is defining a new infrastructure category: Enterprise AI Operating Systems. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Data Science Wizards?** - **Seller:** [Data Science Wizards](https://www.g2.com/sellers/data-science-wizards) - **Year Founded:** 2019 - **HQ Location:** Thane, IN - **LinkedIn® Page:** https://www.linkedin.com/company/data-science-wizards (65 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Data Science Wizards? **"[Overall better experiance](https://www.g2.com/survey_responses/data-science-wizards-review-8439710)"** **Rating:** 4.0/5.0 stars *— MELVIN T.* [Read full review](https://www.g2.com/survey_responses/data-science-wizards-review-8439710) --- ### 5. [DeepDetect](https://www.g2.com/products/deepdetect/reviews) DeepDetect is a deep learning API and server that is written in C++11 to makes deep learning easy to work with and integrate into existing applications. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind DeepDetect?** - **Seller:** [JoliBrain](https://www.g2.com/sellers/jolibrain) - **HQ Location:** Toulouse, - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are G2 Users Discussing About DeepDetect? - [What is DeepDetect used for?](https://www.g2.com/discussions/deepdetect-what-is-deepdetect-used-for) - [What is DeepDetect used for?](https://www.g2.com/discussions/what-is-deepdetect-used-for) ### 6. [FalkorDB](https://www.g2.com/products/falkordb/reviews) An ultra-low latency Graph Database that perfects the Knowledge Graph for GraphRAG. Effectively overcoming the existing limitations of RAG for GenAI & Large Language Models (LLM). **Average Rating:** 4.5/5.0 **Total Reviews:** 7 **How Do G2 Users Rate FalkorDB?** - **Ease of Admin:** 9.0/10 (Category avg: 8.5/10) **Who Is the Company Behind FalkorDB?** - **Seller:** [FalkorDB](https://www.g2.com/sellers/falkordb) - **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:** 100% Small-Business #### What Are Recent G2 Reviews of FalkorDB? **"[Multiple Graphs in One Place Makes Workflows Effortless](https://www.g2.com/survey_responses/falkordb-review-13096610)"** **Rating:** 4.5/5.0 stars *— Albert L.* [Read full review](https://www.g2.com/survey_responses/falkordb-review-13096610) --- **"[Fast, Scalable Graph with Effortless Integration](https://www.g2.com/survey_responses/falkordb-review-13096847)"** **Rating:** 5.0/5.0 stars *— Jamil M.* [Read full review](https://www.g2.com/survey_responses/falkordb-review-13096847) --- ### 7. [Fireflai](https://www.g2.com/products/fireflai/reviews) Fireflai® is a B2B SaaS Data platform that leverages proprietary Data Science, Machine Learning and Generative AI to transform product data operations. Fireflai® enables seamless ingestion, management and enrichment of complex catalogues and product master data - accelerating the onboarding, activation and continuous improvement of SKUs for Manufacturers, Distributors and Retailers. Fireflai can: - Ingest PIM/ERP extracts such as product and parts lists, taxonomies and lists of values - Enrich product lists with validated data, documents and images from unstructured supplier files, catalogues, metadata, even the web - Generate content such as descriptions, taglines, features & benefits, SEO tags - Cleanse, match and merge datasets to create a 'golden product dataset' - Classify and transform product data into required taxonomies for simple ERP/PIM import - Translate datasets into multiple languages **Average Rating:** 4.5/5.0 **Total Reviews:** 5 **How Do G2 Users Rate Fireflai?** - **Natural Language Understanding:** 0.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Fireflai?** - **Seller:** [Fireflai](https://www.g2.com/sellers/fireflai) - **Year Founded:** 2023 - **HQ Location:** Manchester , GB - **LinkedIn® Page:** https://www.linkedin.com/company/fireflai-ai (8 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 60% Enterprise, 20% Mid-Market #### What Are Fireflai's Pros and Cons? **Pros:** - Ease of Use (4 reviews) - Time-Saving (4 reviews) - Speed (3 reviews) - Implementation Ease (2 reviews) - Automation (1 reviews) **Cons:** - Bug Issues (1 reviews) - Inaccuracy (1 reviews) - Inaccuracy Issues (1 reviews) - Software Bugs (1 reviews) ### What Do G2 Reviewers Say About Fireflai? *AI-generated summary from verified user reviews* **Pros:** - Users find Fireflai to be **easy to use** , facilitating efficient data processing and intuitive navigation for all users. - Users value the **time-saving functionality** of Fireflai, enabling them to focus on other important tasks efficiently. - Users love the **speed** of Fireflai for quickly processing data and delivering efficient solutions without integration delays. - Users appreciate the **implementation ease** of Fireflai, which ensures efficient collection of product attributes without complications. - Users commend Fireflai for their **efficient automation** , making implementation quick and integration-free with existing systems. **Cons:** - Users report **bug issues** , particularly hallucinations in results that require double-checking for accuracy, especially with images. - Users note the **occasional hallucinations** in Fireflai's results, necessitating double-checking, particularly with images. - Users report **inaccuracy issues** with Fireflai, requiring double-checking of results, particularly for images. - Users report **occasional hallucinations** in results, necessitating double-checking, particularly with images for accuracy. #### What Are Recent G2 Reviews of Fireflai? **"[Enriching our product data with Fireflai](https://www.g2.com/survey_responses/fireflai-review-12039233)"** **Rating:** 5.0/5.0 stars *— Simon C.* [Read full review](https://www.g2.com/survey_responses/fireflai-review-12039233) --- **"[Efficient Data Scraping with Stellar Support](https://www.g2.com/survey_responses/fireflai-review-12543523)"** **Rating:** 4.5/5.0 stars *— Jessica O.* [Read full review](https://www.g2.com/survey_responses/fireflai-review-12543523) --- ### 8. [Forwrd](https://www.g2.com/products/forwrd/reviews) Struggling to keep your scoring models up to date every time marketing campaigns or product features are launched? Meet Forwrd, the easiest way to build accurate scoring models that literally become smarter every day, learning from new data in real time. Build a self-learning scoring model that automatically identifies new conversion-impacting factors in your data and updates itself, without any manual work. Scores can be pushed into the business apps employees use daily (e.g., SFDC, HubSpot, Slack) to enable frontline employees to focus on their best prospects and customers. ✅ No more manual updates to your scoring method as your marketing evolves. ✅ Your model gets smarter every day by learning from fresh data. ✅ Accurate scoring means focusing on your AAA leads. ✅ Connect your CRM, HubSpot, product analytics, ticketing system, and more! ✅ No need for analysts or data teams. There's a reason B2B SaaS leaders like Jasper.ai, AppsFlyer, and WalkMe use Forwrd to focus on their best leads, MQLs, PQLs, SQLs, and customers. – "4X more PQLs", PowToon – "48% more opportunities", AppsFlyer – "21% more qualified pipeline", Totango – "31% better retention", WalkMe **Average Rating:** 4.8/5.0 **Total Reviews:** 32 **How Do G2 Users Rate Forwrd?** - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Forwrd?** - **Seller:** [Forwrd](https://www.g2.com/sellers/forwrd) - **Year Founded:** 2021 - **HQ Location:** Tel Aviv-Yafo, IL - **LinkedIn® Page:** https://www.linkedin.com/company/74519590 (3 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 66% Mid-Market, 16% Enterprise #### What Are Forwrd's Pros and Cons? **Pros:** - Ease of Use (6 reviews) - Machine Learning (6 reviews) - Features (5 reviews) - Intuitive (4 reviews) - Automation (3 reviews) **Cons:** - Learning Difficulty (1 reviews) ### What Do G2 Reviewers Say About Forwrd? *AI-generated summary from verified user reviews* **Pros:** - Users find Forwrd's interface to be **exceptionally easy to use** , enabling efficient data model development and analysis. - Users appreciate the **ease of building machine learning models** with Forwrd, streamlining data analysis and improving efficiency. - Users praised the **seamless onboarding and integration** of Forwrd, enhancing their experience and efficiency in model building. - Users highlight the **intuitive dashboard** , enabling easy data exploration and efficient machine learning model building. - Users value the **automation capabilities** of Forwrd, which streamline their data analysis and modeling processes efficiently. **Cons:** - Users face **learning difficulties** initially but can overcome them with guidance from support, enabling successful model building. #### What Are Recent G2 Reviews of Forwrd? **"[Access to AI/machine learning for normies!](https://www.g2.com/survey_responses/forwrd-review-10519268)"** **Rating:** 4.5/5.0 stars *— Stuart W.* [Read full review](https://www.g2.com/survey_responses/forwrd-review-10519268) --- **"[Transparent, ML-Powered BI](https://www.g2.com/survey_responses/forwrd-review-10064071)"** **Rating:** 5.0/5.0 stars *— Josh R.* [Read full review](https://www.g2.com/survey_responses/forwrd-review-10064071) --- ### 9. [Gaio DataOS](https://www.g2.com/products/gaio-dataos/reviews) Gaio DataOS is a unified platform that enables developers to build data pipelines, integrations, ETL flows, and analytical models within a high-performance, scalable low-code environment. It includes Data Chat for exploring data through LLMs like OpenAI, Deepseek, and Grok, and uses AI to generate dynamic dashboards that simplify analytical development and speed up insights. As a full Data Operating System, it centralizes all data operations and can process billions of data points in seconds, making it ideal for massive Big Data workloads. The platform features built-in AI, machine learning, and LLM capabilities that support advanced analytics, intelligent agents, and process automation. Gaio promotes data accessibility for users of all skill levels, offering a free, self-hosted Community Edition via Docker to make powerful tools widely available. With visual tools, API integrations, and workflow automation, users can easily manage data, create complex processes, and eliminate repetitive tasks. By consolidating multiple tools into one solution, Gaio DataOS lowers operational costs while enabling efficient team collaboration through shared data, projects, and models. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind Gaio DataOS?** - **Seller:** [Gaio](https://www.g2.com/sellers/gaio) - **Year Founded:** 2017 - **HQ Location:** Houston, US - **LinkedIn® Page:** https://www.linkedin.com/company/gaio (45 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 10. [GitHub Chat All](https://www.g2.com/products/github-chat-all/reviews) GitHub is a leading platform for collaborative software development, offering a suite of tools that enable developers to create, manage, and share code efficiently. Built upon the Git version control system, GitHub provides a centralized space for version control, issue tracking, and project management, facilitating seamless collaboration among developers worldwide. As of May 2025, GitHub boasts a user base of 150 million and hosts over 420 million repositories, solidifying its position as the world's largest source code host. Key Features and Functionality: - Version Control: Utilizes Git to track changes, manage code history, and support branching and merging strategies. - Repository Hosting: Offers both public and private repositories for code storage and collaboration. - Issue Tracking: Provides tools for reporting, tracking, and managing project issues and feature requests. - Pull Requests: Facilitates code reviews and discussions through pull requests, enabling collaborative code improvements. - Continuous Integration and Deployment: Integrates with various CI/CD tools to automate testing and deployment processes. - Wikis and Documentation: Supports project documentation through integrated wikis and README files. - Social Coding: Encourages collaboration with features like following users, starring repositories, and activity feeds. Primary Value and User Solutions: GitHub addresses the complexities of modern software development by providing a unified platform that streamlines collaboration, enhances code quality, and accelerates project timelines. By centralizing code repositories and integrating essential development tools, GitHub enables teams to work cohesively, regardless of geographical barriers. Its robust version control system ensures code integrity and facilitates efficient management of project histories. Additionally, GitHub's emphasis on community engagement and open-source contributions fosters innovation and knowledge sharing, empowering developers to build better software together. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate GitHub Chat All?** - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) **Who Is the Company Behind GitHub Chat All?** - **Seller:** [GitHub](https://www.g2.com/sellers/github) - **Year Founded:** 2008 - **HQ Location:** San Francisco, CA - **Twitter:** @github (2,673,925 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1418841/ (6,106 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of GitHub Chat All? **"[Seamless CLI Integration for Agentic Developer Workflows](https://www.g2.com/survey_responses/github-chat-all-review-13073753)"** **Rating:** 5.0/5.0 stars *— Anas K.* [Read full review](https://www.g2.com/survey_responses/github-chat-all-review-13073753) --- ### 11. [Graphext](https://www.g2.com/products/graphext/reviews) Exploratory Data Analysis and Predictive Modelling. Faster and more powerful insights with no-code. On a mission to build the best no-code data analytics tool. More powerful than dashboards & more intuitive than notebooks. We allow non-technical people to explore their data, and help technical people save time. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Graphext?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.7/10 (Category avg: 8.2/10) **Who Is the Company Behind Graphext?** - **Seller:** [Graphext](https://www.g2.com/sellers/graphext) - **Year Founded:** 2017 - **HQ Location:** Madrid, ES - **LinkedIn® Page:** http://www.linkedin.com/company/graphext (23 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are Recent G2 Reviews of Graphext? **"[Graphext - A multi purpose tool for data exploration and visualization](https://www.g2.com/survey_responses/graphext-review-9408365)"** **Rating:** 4.5/5.0 stars *— Brajesh S.* [Read full review](https://www.g2.com/survey_responses/graphext-review-9408365) --- ### 12. [Harumi](https://www.g2.com/products/harumi/reviews) Harumi.io is an AI platform for Operations Research. Our AI translates business rules from real-economy companies (manufacturing, retail, logistics) into mathematical models and writes the code that will be used to simulate customer scenarios and find the one in which resources are used as efficiently as possible. Learn more at https://harumi.io/. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Harumi?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Harumi?** - **Seller:** [Harumi](https://www.g2.com/sellers/harumi) - **Year Founded:** 2024 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/harumi-io (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Harumi? **"[Using the platform accelerated the operations research tasks inside our company](https://www.g2.com/survey_responses/harumi-review-11385015)"** **Rating:** 5.0/5.0 stars *— Verified User in Health, Wellness and Fitness* [Read full review](https://www.g2.com/survey_responses/harumi-review-11385015) --- ### 13. [HyperAspect Cognitive Cloud](https://www.g2.com/products/hyperaspect-cognitive-cloud/reviews) HyperAspect Cognitive Cloud is an enterprise AI analytics and automation platform that empowers users to leverage big data to drive strategic, efficient decision-making across departments. We bring responsible AI and natural language processing into an organization's core processes with the required security compliance frameworks within data-intensive industries like healthcare, finance, insurance, legal, marketing, retail, professional digital services. **Average Rating:** 5.0/5.0 **Total Reviews:** 6 **How Do G2 Users Rate HyperAspect Cognitive Cloud?** - **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) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind HyperAspect Cognitive Cloud?** - **Seller:** [HyperAspect](https://www.g2.com/sellers/hyperaspect) - **Year Founded:** 2017 - **HQ Location:** Washinghton , US - **LinkedIn® Page:** https://bg.linkedin.com/company/hyperaspect (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Mid-Market, 33% Small-Business #### What Are HyperAspect Cognitive Cloud's Pros and Cons? **Pros:** - AI Capabilities (3 reviews) - AI Integration (3 reviews) - Cloud Computing (3 reviews) - Customer Support (3 reviews) - Easy Integrations (3 reviews) **Cons:** - Expensive (1 reviews) - Pricing Issues (1 reviews) ### What Do G2 Reviewers Say About HyperAspect Cognitive Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **AI-driven insights** of HyperAspect Cognitive Cloud, enhancing their data analysis experience effortlessly. - Users praise the **user-friendly AI integration** of HyperAspect Cognitive Cloud, making advanced data analysis accessible to all. - Users appreciate the **user-friendly design** of HyperAspect Cognitive Cloud, making AI accessible for all skill levels. - Users benefit from the **helpful customer support** that simplifies implementation and enhances productivity with HyperAspect Cognitive Cloud. - Users value the **easy integrations** of HyperAspect Cognitive Cloud for quick deployments and enhanced productivity. **Cons:** - Users find the **pricing expensive** , making it less accessible for smaller businesses seeking cognitive solutions. - Users find the **pricing issues** challenging, especially for smaller businesses looking to adopt HyperAspect Cognitive Cloud. #### What Are Recent G2 Reviews of HyperAspect Cognitive Cloud? **"[Very easy to work with](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10712175)"** **Rating:** 5.0/5.0 stars *— Atanas A.* [Read full review](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10712175) --- **"[Very user friendly and effective Cloud Computing solution](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10716137)"** **Rating:** 5.0/5.0 stars *— Viktor I.* [Read full review](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10716137) --- ### 14. [Hypersonix](https://www.g2.com/products/hypersonix/reviews) Hypersonix AI is a sophisticated solution tailored for the retail industry, designed to assist users in navigating the intricate landscape of competitor analysis, pricing strategies, promotional effectiveness, inventory management, and demand forecasting. By leveraging advanced algorithms specifically developed for the commerce industry, Hypersonix AI empowers retailers to make informed decisions that enhance their operational efficiency and market competitiveness. This product primarily serves retail merchants who seek to gain a deeper understanding of their market dynamics and optimize their business strategies to maximize product margins. With the retail environment becoming increasingly complex, the need for actionable intelligence is critical. Hypersonix AI serves as a vital tool for retailers looking to analyze their competitors' activities, understand pricing trends, and evaluate the effectiveness of their promotions. By harnessing the power of data, retailers can identify opportunities for growth and make strategic decisions that align with their business objectives. Key features of Hypersonix AI include real-time competitor analysis, dynamic pricing recommendations, and comprehensive inventory forecasting. The platform provides users with up-to-date intelligence into competitor pricing and promotional strategies, allowing retailers to adjust their own approaches accordingly. Additionally, the system offers predictive analytics that assists merchants in forecasting inventory needs based on historical data and market trends, ensuring they can meet customer demand without overstocking. The benefits of utilizing Hypersonix AI extend beyond just data analysis; it fosters a culture of data-driven decision-making within organizations. Retailers can accelerate their decision-making processes, enabling them to respond swiftly to market changes, capitalize on emerging trends and transform their commerce strategies. By maximizing profit margins and enhancing revenue growth, Hypersonix AI positions retailers to be more competitive in a fast-paced market environment. The integration of actionable insights from ProfitGPT further enriches the user experience, generating tailored GPT recommendations that align with specific business goals. In essence, Hypersonix AI stands out in the retail analytics category by offering a comprehensive suite of features that address the unique challenges faced by merchants. Its focus on actionable insights and advanced algorithms tailored for the commerce industry makes it an essential tool for retailers aiming to thrive in an increasingly competitive landscape. **Average Rating:** 4.8/5.0 **Total Reviews:** 12 **How Do G2 Users Rate Hypersonix?** - **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:** 9.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Hypersonix?** - **Seller:** [Hypersonix](https://www.g2.com/sellers/hypersonix) - **Year Founded:** 2018 - **HQ Location:** San Jose, US - **LinkedIn® Page:** https://www.linkedin.com/company/hypersonix-ai/ (110 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Retail - **Company Size:** 42% Mid-Market, 42% Small-Business #### What Are Recent G2 Reviews of Hypersonix? **"[VP of Marketing & Merchandising](https://www.g2.com/survey_responses/hypersonix-review-9509562)"** **Rating:** 5.0/5.0 stars *— Kim S.* [Read full review](https://www.g2.com/survey_responses/hypersonix-review-9509562) --- **"[Great product but even better customer service](https://www.g2.com/survey_responses/hypersonix-review-9791365)"** **Rating:** 4.5/5.0 stars *— Tyler C.* [Read full review](https://www.g2.com/survey_responses/hypersonix-review-9791365) --- ### 15. [IBM Watson Machine Learning Accelerator](https://www.g2.com/products/ibm-watson-machine-learning-accelerator/reviews) Watson Machine Learning Accelerator Enterprise makes deep learning and machine learning more accessible to your staff, and the benefits of AI more obtainable for your business. It combines popular open source deep learning frameworks, efficient AI development tools, and accelerated IBM Power Systems servers. Now your organization can deploy a fully optimized and supported AI platform that delivers blazing performance, proven dependability and resilience. IBM PowerAI Enterprise is a complete environment for data science as a service, enabling your organization to bring new applied AI applications into production. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate IBM Watson Machine Learning Accelerator?** - **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 IBM Watson Machine Learning Accelerator?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of IBM Watson Machine Learning Accelerator? **"[A Great business tool which help in growth](https://www.g2.com/survey_responses/ibm-watson-machine-learning-accelerator-review-5231431)"** **Rating:** 5.0/5.0 stars *— KESHAV K.* [Read full review](https://www.g2.com/survey_responses/ibm-watson-machine-learning-accelerator-review-5231431) --- #### What Are G2 Users Discussing About IBM Watson Machine Learning Accelerator? - [What is IBM Watson Machine Learning Accelerator used for?](https://www.g2.com/discussions/what-is-ibm-watson-machine-learning-accelerator-used-for) ### 16. [iMerit Ango Hub Multimodal AI Platform](https://www.g2.com/products/imerit-ango-hub-multimodal-ai-platform/reviews) For organizations driving advancements in traditional AI and generative AI, iMerit delivers comprehensive, software-delivered solutions that encompass high-quality data annotation, enrichment, and model fine-tuning across multimodal datasets, including text, images, audio, video, and sensor data. By combining state-of-the-art technology with human expertise, iMerit empowers businesses to develop precise AI models, from traditional supervised learning systems to cutting-edge generative AI applications. Unlike generic service providers, iMerit specializes in secure, scalable, and domain-specific solutions, enabling innovation and performance in the most demanding AI and machine learning initiatives. For developers of traditional AI applications iMerit provides best in class data annotation tooling, workflow automation and a highly skilled workforce within a single end-to-end solution. The unique combination of technology, talent and techniques produces the highest quality data in the industry for machine learning. For developers of Generative AI applications iMerit provides the tools, automation and domain experts for accurate model evaluation and fine turning. The solution combines the technology and human-in-the-loop domain experts for all forms of supervised reinforcement learning. Services include corpus creation, data augmentation, RLHF, RAG fine tuning, chain of thought reasoning and red-teaming for greater model precision. Visit www.imerit.net to learn more. **Average Rating:** 4.8/5.0 **Total Reviews:** 11 **How Do G2 Users Rate iMerit Ango Hub Multimodal AI Platform?** - **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) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind iMerit Ango Hub Multimodal AI Platform?** - **Seller:** [iMerit Technology](https://www.g2.com/sellers/imerit-technology) - **Year Founded:** 2012 - **HQ Location:** San Jose, US - **Twitter:** @iMeritDigital (1,654 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/imerit (6,415 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 25% Enterprise #### What Are iMerit Ango Hub Multimodal AI Platform's Pros and Cons? **Pros:** - AI Integration (1 reviews) - Annotation Efficiency (1 reviews) - Customization (1 reviews) - Data Accuracy (1 reviews) - Machine Learning (1 reviews) **Cons:** - Complexity (1 reviews) - Steep Learning Curve (1 reviews) ### What Do G2 Reviewers Say About iMerit Ango Hub Multimodal AI Platform? *AI-generated summary from verified user reviews* **Pros:** - Users value the **AI integration capabilities** of iMerit Ango Hub, enhancing data synchronization and overall project efficiency. - Users value the **annotation efficiency** of iMerit Ango Hub, enhancing productivity in complex AI/ML projects. - Users value the **customization options** of iMerit Ango Hub, enabling tailored solutions for diverse AI/ML needs. - Users value the **high-quality labeled data** provided by iMerit Ango Hub, enhancing their complex AI/ML projects. - Users appreciate the **robust machine learning capabilities** of Ango Hub, enabling effective data handling for complex projects. **Cons:** - Users find the **complexity of the platform** necessitates extensive training, making it less accessible for some users. - Users find the platform's **steep learning curve** necessitates dedicated training for effective usage and understanding. #### What Are Recent G2 Reviews of iMerit Ango Hub Multimodal AI Platform? **"[Performant for video-based tasks](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-5412382)"** **Rating:** 5.0/5.0 stars *— Ezra O.* [Read full review](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-5412382) --- **"[Easy to use. Very intuitive](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-10867815)"** **Rating:** 5.0/5.0 stars *— Verified User in Automotive* [Read full review](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-10867815) --- #### What Are G2 Users Discussing About iMerit Ango Hub Multimodal AI Platform? - [What is Ango Hub used for?](https://www.g2.com/discussions/what-is-ango-hub-used-for) ### 17. [Infoveave](https://www.g2.com/products/infoveave/reviews) Infoveave is an AI-powered unified data platform that helps enterprises automate data pipelines, improve data quality, enable predictive analytics, and turn insights into measurable business actions — all within a single environment. Unlike traditional BI tools or standalone ETL platforms, Infoveave connects the entire data lifecycle from ingestion and transformation to governance, analytics, and operational execution. Infoveave is built on a strong foundation of data governance and data security, ensuring consistent data management, compliance, and protection. With capabilities like data lineage tracking, metadata management, row-level security, and audit trails, the platform helps maintain data integrity and control. Key Features Fovea - AgenticAI Assistant Infoveave's AgenticAI, Fovea, is embedded across the platform. Fovea assists in building data transformations, suggesting insights, automating workflows, and simplifying advanced analytics, reducing technical dependency and improving cross-team adoption. Data Automation & Integration • 50+ native connectors (cloud apps, databases, warehouses) • Automated data ingestion & transformation • Workflow orchestration with monitoring & alerts • Real-time pipeline visibility AI-Powered Analytics & Predictive Modeling • AutoML for predictive insights • What-if scenario planning • Python integration for advanced modeling • API-accessible analytics endpoints Conversational Dashboards & Self-Service BI • Natural language queries • 100+ interactive visuals • Drill-down exploration • Scheduled & automated reporting Built-in Data Quality & Governance • Automated validation & anomaly detection • Data catalog & lineage tracking • Role-based access control • Audit trails & governance workflows Data Apps & Operational Workflows • Low-code applications • Integrated data capture forms • Automated decision triggers • Insight-to-action workflows Business Value • Faster deployment of data pipelines • Improved data accuracy and trust • Reduced reliance on multiple disconnected tools • Faster decision-making cycles • Measurable operational efficiency Infoveave unifies data automation, AI-powered analytics, governance, and operational workflows into one intelligent platform — turning enterprise data into trusted, actionable decisions. **Average Rating:** 4.9/5.0 **Total Reviews:** 9 **How Do G2 Users Rate Infoveave?** - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Infoveave?** - **Seller:** [Noesys Software](https://www.g2.com/sellers/noesys-software) - **HQ Location:** N/A - **Twitter:** @infoveave (15 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/infoveave-pty-ltd/ (3 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 44% Small-Business, 22% Enterprise #### What Are Infoveave's Pros and Cons? **Pros:** - Ease of Use (3 reviews) - Easy Integrations (3 reviews) - Features (3 reviews) - Community Support (2 reviews) - Customer Support (2 reviews) **Cons:** - Poor Customer Support (2 reviews) - Dashboard Issues (1 reviews) - Data Cleaning (1 reviews) - Data Inaccuracy (1 reviews) - Data Integration (1 reviews) ### What Do G2 Reviewers Say About Infoveave? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of Infoveave, enabling quick data import and efficient dashboard creation. - Users value the **easy integrations** of Infoveave, enabling quick data import and seamless dashboard creation. - Users appreciate the **ease of use and quick implementation** of Infoveave, enhancing data management and insights delivery. - Users value the **excellent community support** of Infoveave, enhancing their experience and boosting satisfaction. - Users commend the **excellent customer support** of Infoveave, highlighting its reliability and efficient assistance. **Cons:** - Users find the **poor customer support** lacking in helpful resources, making it difficult to navigate the product effectively. - Users face **dashboard issues** that complicate data isolation and require extensive restructuring for multi-tenant reports. - Users face **initial difficulties with data cleaning** , complicating the use of dashboards for multi-tenant reports. - Users face significant **data inaccuracy issues** with Infoveave, struggling with isolated tenant reports from multi-tenant sources. - Users face **initial difficulties with data integration** , needing extensive restructuring for effective dashboard functionality across tenants. #### What Are Recent G2 Reviews of Infoveave? **"[Scalable data automation for multi-site operations: Our unified source of truth](https://www.g2.com/survey_responses/infoveave-review-12599874)"** **Rating:** 5.0/5.0 stars *— Satish G.* [Read full review](https://www.g2.com/survey_responses/infoveave-review-12599874) --- **"[Infoveave Delivers Powerful, User-Friendly Shopfloor Intelligence](https://www.g2.com/survey_responses/infoveave-review-12600038)"** **Rating:** 5.0/5.0 stars *— Chore R.* [Read full review](https://www.g2.com/survey_responses/infoveave-review-12600038) --- ### 18. [JARVIS Video Analytics Solution](https://www.g2.com/products/jarvis-video-analytics-solution/reviews) Staqu is an AI research company with more than 7 years of experience in finding solutions to challenging business processes. Multiple private owned companies and law enforcement agencies use our products on a daily basis across the world. Multiple organisations have chosen our JARVIS video analytics solution because it is versatile and modular. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate JARVIS Video Analytics Solution?** - **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 JARVIS Video Analytics Solution?** - **Seller:** [JARVIS Video Analytics Solution](https://www.g2.com/sellers/jarvis-video-analytics-solution) - **Year Founded:** 2015 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/staqu-technologies (100 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are Recent G2 Reviews of JARVIS Video Analytics Solution? **"[Jarvis helps in Audio-Video management](https://www.g2.com/survey_responses/jarvis-video-analytics-solution-review-8700779)"** **Rating:** 5.0/5.0 stars *— Maithili G.* [Read full review](https://www.g2.com/survey_responses/jarvis-video-analytics-solution-review-8700779) --- ### 19. [Kaskada](https://www.g2.com/products/kaskada/reviews) Kaskada is an innovative, Seattle-based, machine learning company that is leveling up the data science and machine learning industries. We are the company that first solved temporal streaming joins, enabling running predictive models from event-based data. Using Kaskada enables customers to get more value from event-based data. Now, users can build models that weren't previously possible, that will actually work once put in production–without leakage. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Kaskada?** - **Application:** 6.7/10 (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 Kaskada?** - **Seller:** [Kaskada](https://www.g2.com/sellers/kaskada) - **Year Founded:** 2016 - **HQ Location:** Seattle, US - **LinkedIn® Page:** https://www.linkedin.com/company/18939102 (2 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 20. [Kepler](https://www.g2.com/products/kepler/reviews) Advanced Self-Serve AI and AutoML Platform. No Machine Learning (ML) Experience Required. The Kepler platform enables you to bring AI and Machine Learning (ML) projects to market faster with your existing teams and technological investments. It does this by combining advanced ML — including Deep Learning — with intuitive design, all within a self-serve framework built to help you create, train, and deploy AI projects, fast. This helps accelerate AI adoption by automating the end-to-end ML process, effectively enabling users with no ML experience to leverage cutting-edge ML capabilities to solve hundreds of business-critical use cases, including: Demand Forecasting, Churn Prediction, Lifetime Value Prediction, Predictive Maintenance, Sentiment Analysis, Anomaly Detection, Session / UX Optimization, and User Intent Prediction. The Automated Data Science Workflows within the Kepler platform automate the complex and time-consuming data science steps that exist across the ML process so that users can produce AI models to predict with accuracy, forecast with precision, and illuminate new insights. The Kepler platform’s ML capabilities are constantly optimized and seamlessly integrate with key production environments. It is optimized for Azure, and leverages Azure Kubernetes Services (AKS) and Microsoft's compute platform. Core Features of the Kepler Platform: - End-to-end Automation: Benefit from our advanced, automated workflows so that all you need to leverage the power of ML is data and a business challenge to get started. - Extensive Workflow Library: From forecasting demand to predictive maintenance to customer segmentation, Kepler generates impactful results for a range of use cases. The Kepler platform’s Automated Data Science Workflows include: Tabular Classification, Time Series Forecasting, Tabular Regression, Clustering, Anomaly Detection, User-item Recommender, Text Classification, and Image Classification. - More Data Types: Leverage your tabular, text and image data to generate solutions, getting more value from your data, and allowing you to address more business problems. - Quick & Secure Deployment: Yield measurable returns from your ML investment by rapidly deploying Kepler models to your key environments via RESTful APIs. Who Benefits from Using the Kepler Platform? - Business Leader: Scale AI and accelerate time-to-value with a platform that grows with your business using the team and skills already within your organization. - Business User: Augment your productivity and address complex business problems with advanced AI and ML capabilities without the need for technical ML experience. - Data Scientist & IT: Leverage state-of-the-art, end-to-end automation, an extensive library of AI algorithms, and the ability to quickly deploy machine learning models. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Kepler?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 0.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 0.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Kepler?** - **Seller:** [Stradigi AI](https://www.g2.com/sellers/stradigi-ai) - **Year Founded:** 2017 - **HQ Location:** Montreal, CA - **LinkedIn® Page:** http://www.linkedin.com/company/stradigiai (8 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of Kepler? **"[Kepler provides amazing geospatial visualizations](https://www.g2.com/survey_responses/kepler-review-8488712)"** **Rating:** 4.5/5.0 stars *— Suleman B.* [Read full review](https://www.g2.com/survey_responses/kepler-review-8488712) --- ### 21. [Kleene](https://www.g2.com/products/kleene/reviews) Kleene.ai unifies your business data in one place to power real-time reporting, analytics, and AI-driven decisions. Go live in weeks, not months — without building a data team. Kleene.ai gives mid-market and enterprise businesses a single, intelligent platform to understand what drives growth. It connects to 200+ data sources — from CRMs and ERPs to marketing and finance tools — and automatically cleans, combines, and models your data for reporting, analytics, and forecasting. At the core of the platform is KAI: an AI-powered analytics layer that surfaces insights across your business, and a conversational AI assistant that lets teams query their data in plain language — no SQL, no analysts, no waiting. Whether you're investigating churn, margins, or customer lifetime value, KAI puts answers directly in the hands of the people who need them. Kleene.ai is built for organizations that want to make faster, data-driven decisions without the complexity of managing disparate tools or large engineering teams. Every implementation is tailored to your existing data environment — with transparent pricing and a clear path to value. Why Kleene: 1️⃣ Fast to implement — adapts to any data architecture and goes live in weeks, not months. 2️⃣ No engineering overhead — a fully managed platform that eliminates the need for a large data team, cutting infrastructure costs by up to 80%. 3️⃣ AI-powered from day one — KAI's analytics layer and LLM assistant turn complex data into clear, actionable answers across finance, marketing, and operations. 4️⃣ Tailored to your business — every setup aligns with your existing environment and goals, backed by transparent pricing and hands-on support. Kleene.ai replaces the fragmented, manual approach to data with a single platform that thinks alongside your team. The result: faster insights, sharper decisions, and measurable business impact. **Average Rating:** 4.6/5.0 **Total Reviews:** 29 **How Do G2 Users Rate Kleene?** - **Ease of Admin:** 8.6/10 (Category avg: 8.5/10) **Who Is the Company Behind Kleene?** - **Seller:** [kleene.ai](https://www.g2.com/sellers/kleene-ai) - **Company Website:** https://kleene.ai/ - **Year Founded:** 2017 - **HQ Location:** London, London - **Twitter:** @Kleene_ai (32 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kleeneai/ (35 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 52% Mid-Market, 34% Small-Business #### What Are Kleene's Pros and Cons? **Pros:** - Ease of Use (3 reviews) - Automation (2 reviews) - Business Value (2 reviews) - Cost-Effective (2 reviews) - Customer Support (2 reviews) ### What Do G2 Reviewers Say About Kleene? *AI-generated summary from verified user reviews* **Pros:** - Users highlight the **ease of use** of Kleene, appreciating its intuitive interface and seamless onboarding process. - Users praise the **automation capabilities** of Kleene, significantly reducing manual reporting and streamlining data management. - Users value Kleene for its **efficient data handling and intuitive interface** , streamlining reporting and saving significant time. - Users praise Kleene for its **cost-effectiveness** , significantly reducing time spent on manual reporting and ensuring transparency in pricing. - Users commend Kleene's **exceptional customer support** , highlighting quick responses and knowledgeable assistance throughout their implementation process. #### What Are Recent G2 Reviews of Kleene? **"[Changed my life, love it!](https://www.g2.com/survey_responses/kleene-review-7138871)"** **Rating:** 5.0/5.0 stars *— Alisha S.* [Read full review](https://www.g2.com/survey_responses/kleene-review-7138871) --- **"[A partner who builds with you](https://www.g2.com/survey_responses/kleene-review-12919267)"** **Rating:** 5.0/5.0 stars *— Emil K.* [Read full review](https://www.g2.com/survey_responses/kleene-review-12919267) --- ### 22. [Macky](https://www.g2.com/products/macky/reviews) Macky is the first AI business consulting platform that offers any organization an easy, non-prompt-based AI answers for any business question using OpenAI technology. Companies can reduce their basic consulting costs by 90% by using Macky. Plans start as low as $10 per month. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Macky?** - **Seller:** [Kinetic Consultancy Services](https://www.g2.com/sellers/kinetic-consultancy-services) - **Year Founded:** 2013 - **HQ Location:** Dubai, AE - **LinkedIn® Page:** https://www.linkedin.com/company/6640547 (8 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Macky? **"[A great tool for any business](https://www.g2.com/survey_responses/macky-review-10393639)"** **Rating:** 5.0/5.0 stars *— Joe T.* [Read full review](https://www.g2.com/survey_responses/macky-review-10393639) --- ### 23. [MainlyAI platform](https://www.g2.com/products/mainlyai-platform/reviews) All-purpose AI platform for Gen AI and beyond. It's best suited for rapid protoyping, yet offers Enterprise scalability. Low-code/full-code node-based solution, which means you can work and create AI solutions with minimal coding, yet have the flexibility to tweak, modify and build your own nodes. Endless possibilities! **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate MainlyAI platform?** - **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 MainlyAI platform?** - **Seller:** [MainlyAI](https://www.g2.com/sellers/mainlyai) - **Year Founded:** 2020 - **HQ Location:** Stockholm , SE - **LinkedIn® Page:** https://www.linkedin.com/company/mainly-ai/ (7 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are MainlyAI platform's Pros and Cons? **Pros:** - Customer Support (1 reviews) - Flexibility (1 reviews) - Model Variety (1 reviews) **Cons:** - Insufficient Learning Resources (1 reviews) ### What Do G2 Reviewers Say About MainlyAI platform? *AI-generated summary from verified user reviews* **Pros:** - Users value the **exceptional customer support** from MainlyAI, enhancing their experience with customizable options and pipelines. - Users value the **flexibility** of MainlyAI, appreciating customizable pipelines and extensive support options from staff. - Users value the **model variety** in MainlyAI, appreciating the customizable pipelines and extensive configurable options. **Cons:** - Users find the **insufficient learning resources** on MainlyAI challenging, wishing for more concise documentation to aid their experience. #### What Are Recent G2 Reviews of MainlyAI platform? **"[feels like a glimpse into the future](https://www.g2.com/survey_responses/mainlyai-platform-review-10340748)"** **Rating:** 5.0/5.0 stars *— Verified User in Primary/Secondary Education* [Read full review](https://www.g2.com/survey_responses/mainlyai-platform-review-10340748) --- ### 24. [Nivaris](https://www.g2.com/products/nivaris/reviews) Nirvaris Host is a group of computer programmers, system administrators, web-designers and web-developers who got tired of all of the NONSENSE and non-performing hosting services that are out there around the world. They have extensive experience in developing websites and web-applications, as well as consuming hosting services. Working with a team that can really understand clients' problems and needs from the very first email you send, no non-sense questions are asked. Having been through some very bad experiences, the founder of Nirvaris Host, Software Engineer and full-stack developer Juliano Binder, decided to start a Web Hosting service on his own with the aim of offering a service like no other. The company uses servers with SSD drives only with the top specs we can get, limiting hosting data and the number of accounts for each server, which guarantees superior performance for your services. Hosting plans are designed to fit the needs of pro developers, webmasters, bloggers, business owners and the likes. As valued customers, you will have a full featured and well known CPanel, a friendly client area to manage your bills and everything unlimited. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Nivaris?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Nivaris?** - **Seller:** [Nirvaris HOST](https://www.g2.com/sellers/nirvaris-host) - **Year Founded:** 2015 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/nirvaris/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Nivaris? **"[Simple yet Useful](https://www.g2.com/survey_responses/nivaris-review-8734408)"** **Rating:** 5.0/5.0 stars *— Vince M.* [Read full review](https://www.g2.com/survey_responses/nivaris-review-8734408) --- ### 25. [OPUS](https://www.g2.com/products/vroc-opus/reviews) OPUS is a leading industrial no-code AI platform that allows users to model processes and equipment to identify opportunities for optimization and predictive maintenance. OPUS's real-time insights allow your team to make informed business decisions at every step. Without any programming or coding experience teams can build models to: - Predict when the next failure of undesirable event will occur, - Predict what a value will be in the future, - Identify the root cause of an event, - Identify when equipment or process is degrading or not operating correctly, - Predict when equipment maintenance is required, - Identify opportunities to reduce power consumption, - Identify opportunities to improve productivity, - Optimize settings to improve operational outcomes. Dive deeper into your asset's data than ever before. Discover unexpected correlations that existed unnoticed, and root cause analysis down into individual component level, so you can focus your maintenance efforts. Designed as an enterprise solution, for a holistic view across all plants and facilities. Users can build their own dashboards, set up alerts and stay updated at all times, as macro or micro as they wish. OPUS can be deployed within four weeks and there are no limitations to the number of models you can develop, or individual user costs. Models can be built and deployed in minutes, refreshed based on live operational data continuously. These features allow you to unleash the power of your operational data and experience ROI in next to no time. **Average Rating:** 3.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate OPUS?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 3.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind OPUS?** - **Seller:** [VROC](https://www.g2.com/sellers/vroc) - **Year Founded:** 2016 - **HQ Location:** East Perth, AU - **Twitter:** @vrocai (60 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/vroc-artificial-intelligence (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Small-Business #### What Are Recent G2 Reviews of OPUS? **"[Opus for state inspections.](https://www.g2.com/survey_responses/opus-review-10732075)"** **Rating:** 4.0/5.0 stars *— Joe G.* [Read full review](https://www.g2.com/survey_responses/opus-review-10732075) --- ## 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.