Machine Learning Platform Companies and AutoML Vendors List

Altair Engineering Inc. is a multinational technology company based in Troy, Michigan. Founded in 1985, Altair specializes in software and cloud solutions for simulation, artificial intelligence, high-performance computing, and data analytics. The company operates globally with 71 offices in 24 countries and employs over 2,000 engineers, serving more than 16,000 organizations. Altair's offerings include a range of open-architecture solutions that support the entire product lifecycle. Key products include Altair HyperWorks for simulation and design, Altair RapidMiner for data analytics, and Altair One, a cloud platform for data management and visualization. The company also provides high-performance computing solutions through Altair HPCWorks and PBS GridWorks, along with client engineering services for product development and training. Altair's solutions cater to various industries, including automotive, aerospace, electronics, and life sciences, enhancing efficiency and innovation across sectors.

Banana is a serverless GPU hosting platform that simplifies the deployment and scaling of machine learning models. The company focuses on reducing infrastructure complexity and accelerating time-to-market for machine learning companies by providing managed GPU compute resources with automatic scaling capabilities. Banana's core product is serverless GPU inference hosting. It features fast model loading, enabling large models to load up to 99% faster, and offers significant cost savings on cloud compute. The platform automatically scales GPU resources based on real-time demand, which helps eliminate over-provisioning costs. Additionally, it provides a developer-friendly SDK that allows for model deployment with minimal code. For cases requiring consistent availability, Banana also offers price-matched always-on GPU instances. The platform is designed for machine learning companies and developers who need to deploy models efficiently, particularly those with shorter deployment timelines.

Beam is an on-demand AI compute platform designed for developers to create AI and machine learning products without the hassle of managing servers or complex infrastructure. It operates as an open-source serverless cloud, enabling users to run functions, REST APIs, task queues, and sandboxes on both CPUs and GPUs, with containers launching in under one second. The platform features serverless GPU/CPU compute that containerizes user code and automatically scales for demanding AI workloads, including large language model inference and training. Beam also offers a stateful agent framework for building AI agents, preview environments for instant containerization, and deployment tools that allow users to define infrastructure in code without the need for YAML. Beam serves thousands of developers globally, including notable customers like Geospy, which successfully scaled to 3 million inference requests in one month using Beam's services. For organizations needing additional support, Beam provides enterprise solutions and direct access to its founders.

BigML, Inc. is a technology company based in Corvallis, Oregon, specializing in Machine Learning as a Service (MLaaS). Founded in January 2011, BigML aims to simplify the complexities of Machine Learning, making it accessible to a wide range of users, including analysts, developers, and scientists. The company serves a global customer base of over 225,000 users across various industries such as healthcare, finance, retail, and logistics. BigML offers a comprehensive suite of cloud-based and on-premises services that streamline the Machine Learning lifecycle. Key features include WhizzML, an automated workflow engine for building predictive models, and a user-friendly dashboard for data exploration and visualization. The platform supports various Machine Learning tasks, including classification, regression, time series forecasting, and anomaly detection. BigML also promotes educational initiatives to train the next generation of Machine Learning experts, reinforcing its mission to make Machine Learning beautifully simple for everyone.

Continual is a San Francisco-based AI startup founded in 2019. The company offers an operational AI platform that simplifies the deployment and maintenance of predictive models within cloud data warehouses. Its core product is a warehouse-native AI platform that enables data teams to automatically generate and maintain predictions, such as customer churn, inventory forecasts, and sales predictions, without the need to move or duplicate data. The platform features a simple declarative workflow and end-to-end automation. In addition to predictive modeling, Continual has developed an AI agent platform that allows product and engineering teams to create conversational experiences, AI automations, and intelligent agents. This platform integrates seamlessly with enterprise systems to automate workflows and optimize operations. Continual primarily serves businesses and organizations with modern data stacks, focusing on data, product, and engineering teams. The platform supports integration with major cloud data warehouses like Snowflake, BigQuery, and Databricks. The company has raised approximately $18.5M–$22M in funding, including a $14.5M Series A round.

Machine learning infrastructure https://databricks.com/blog/2022/04/25/cortex-labs-databricks-model-serving-mlops.html

Deepnote is a collaborative, cloud-based AI workspace and data science platform tailored for data professionals. Founded in 2019, it provides a Jupyter-compatible notebook that integrates coding, querying, visualization, and sharing into a single environment. Deepnote supports real-time collaboration, allowing teams to explore data, create interactive charts, and build machine learning models together. The platform features tools for data exploration and analysis, enabling organizations to manage AI agents and workflows efficiently. Deepnote connects to various data sources, including Snowflake, and offers a completely free education workspace for students, teachers, and researchers. Its focus on real-time collaboration, agentic workflows, and hybrid flexibility makes it a valuable resource for enterprise data teams and individuals looking to enhance their data science skills.

FloydHub, also known as Floyd Labs, is a cloud platform that provides GPU-powered infrastructure for machine learning workflows. It is designed to support users who may not have access to high-end local hardware. The platform streamlines machine learning pipelines by separating data and source code management, making it easier to manage projects. Key features of FloydHub include persistent workspaces that function like a cloud deep learning computer, allowing users to preserve files across sessions and switch between CPU and GPU instances. The platform offers a cloud IDE powered by JupyterLab for interactive development, along with tools for dataset management and running training jobs. Users can easily initialize projects, manage datasets, and utilize a command-line tool (floyd-cli) for various tasks. FloydHub is particularly suited for small to medium projects and provides documentation to assist users in getting started with deep learning.

Gradient AI is an InsurTech company based in Boston, Massachusetts, founded in 2018. The company specializes in providing artificial intelligence (AI) solutions tailored for the insurance industry. Gradient AI operates as a Software-as-a-Service (SaaS) platform, utilizing a vast data lake that includes millions of policies and claims to deliver insights that help insurers predict risk and enhance profitability. The company's core offerings include AI-driven underwriting and claims management solutions. These solutions aim to improve risk assessment, streamline policy pricing, and automate claims processes. Gradient AI also provides predictive analytics tools that integrate with existing systems to enhance decision-making efficiency. By the end of 2022, Gradient AI had experienced significant growth, serving over 100 customers in various sectors, including Group Health, Workers' Compensation, and Property & Casualty insurance.

Hex is an AI analytics platform that offers a collaborative data workspace for both technical and non-technical users. Founded in 2019 in San Francisco by former Palantir executives, Hex enables teams to connect to data, analyze it using SQL, Python, or no-code tools, and share their findings through interactive data applications. The platform features collaborative notebooks for writing and running code, tools for building interactive data apps, and capabilities for natural language queries. Hex supports data teams from startups to Fortune 500 companies, including notable clients like Reddit, Notion, and Figma. With a mission to enhance collaboration among data scientists, analysts, and business stakeholders, Hex is positioned as a comprehensive solution for modern data analytics.

Kedro is an open-source Python framework designed for building production-ready data pipelines in data engineering and data science. Developed by QuantumBlack, McKinsey’s advanced analytics firm, Kedro applies software engineering best practices to create reproducible, maintainable, and modular data and machine-learning pipelines. It was first released in June 2019 and is now hosted by the Linux Foundation for vendor-neutral governance. Kedro offers a range of features, including a command-line interface for project scaffolding, a data catalog for configuration-driven access, and a pipeline API for defining workflows. It also includes Kedro-Viz, an interactive tool for visualizing pipelines, and automated documentation generation based on code structure. The framework is supported by an active open-source community and is used by organizations such as McKinsey, NASA, and Rolls-Royce, making it a valuable resource for data scientists, data engineers, and machine-learning engineers transitioning from experimentation to production in analytics or AI projects.

Kumo is a privately held AI model company based in Mountain View, California, founded in 2021. The company offers a SaaS platform designed for training and running advanced AI models directly on relational data, eliminating the need for feature engineering. Kumo's leadership includes experts in Relational Deep Learning and Graph Transformers, with a mission to enhance the utility of relational databases for AI teams. The Kumo platform enables predictions on various types of relational data, such as customer and transaction information. Its proprietary Relational Foundation Model, KumoRFM, provides zero-shot predictions for applications like churn analysis and fraud detection. Kumo also offers solutions for rapid prototyping and ad-hoc predictive inquiries, supporting a range of graph formats and integrating seamlessly with cloud data warehouses. Trusted by notable companies like Reddit, Coinbase, and DoorDash, Kumo's models are utilized in applications that reach hundreds of millions of users worldwide.

MLflow is an open-source platform for managing the complete machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow supports both traditional ML and generative AI workflows: - MLflow Tracking: Record and query experiments, including code, data, config, and results. Now with integrated tracing for GenAI workflows across multiple frameworks. - MLflow Models: Deploy machine learning models in diverse serving environments, with GenAI support for ChatModels and streaming interfaces. - Model Registry: Store, annotate, discover, and manage models in a central repository. - MLflow Evaluation: Evaluate model performance using customizable metrics, including LLM-as-judge frameworks and GenAI-specific benchmarks. - MLflow Deployments: Simplify model deployment and serving across various platforms, with expanded capabilities for hosting large language models. Subscribe to our luma calendar for updates about meetups, office hours, and other events: https://lu.ma/mlflow View code on GitHub here: https://github.com/mlflow/mlflow/ To discuss or get help, please join our mailing list mlflow-users@googlegroups.com

Modelbit makes it easy for machine learning engineers to deploy any ML model to on-demand infrastructure with one git push command.

NannyML is a Belgium-based machine learning monitoring platform founded in 2020. The company focuses on post-deployment data science, helping data scientists estimate model performance in production without needing access to ground truth data. NannyML specializes in detecting data and concept drift, addressing the challenges of silent model failures and performance degradation. NannyML offers a dual-product strategy that includes an open-source Python library, NannyML OSS, and a cloud-based SaaS platform, NannyML Cloud. The OSS tool is model-agnostic and supports various tabular use cases, while the cloud platform provides advanced observability, automated alerting, and interactive visualizations. NannyML integrates with Amazon SageMaker and works with multiple machine learning frameworks, making it suitable for data science teams across different industries. The platform is designed for enterprises that require continuous model observability to support informed business decisions.

Pecan AI is an Israeli company that specializes in Generative AI and Predictive Analytics. Founded in 2018 by Dr. Zohar Bronfman and Noam Brezis, the company is headquartered in Ramat Gan, Tel Aviv. Pecan AI operates as a privately held B2B SaaS provider, employing around 51 to 200 people and has raised $117.5 million in funding. The company offers a Predictive Analytics Platform that integrates Generative AI, AutoML, and automated feature engineering. This platform is designed for business teams, allowing users to create predictive models without needing coding skills. Key features include a no-code/low-code interface, automated feature engineering, and a predictive co-pilot to assist users in modeling tasks. Pecan AI serves various industries, including retail, financial services, telecommunications, and consumer brands, helping organizations optimize operations and enhance decision-making through data-driven insights.

Rasgo is a New York-based software company founded in 2020 that specializes in generative AI-powered self-service analytics for enterprise data warehouses. The company enables business users to query data using natural language and receive automated insights without human intervention. Rasgo was established by Jared Parker and Patrick Dougherty to enhance data insights and streamline enterprise data workflows. The company's primary offering, Rasgo AI, integrates GPT-4 into enterprise data environments to automate analytical strategy development. Key features include GPT-orchestrated agents that generate insights and identify outliers, natural language query conversion to SQL, and automated semantic layer creation. Rasgo also provides a feature store workflow platform and data exploration tools that enhance data preparation and analysis. Rasgo targets global enterprises across various sectors, including finance, manufacturing, biotech, retail, and alternative energy. Its solutions are designed for organizations looking to leverage AI for intelligent data analysis and efficient decision-making, particularly benefiting data teams and business users seeking reliable insights from complex data warehouses.

Saturn Cloud is a cloud platform designed for data science and machine learning (ML) development, deployment, and data pipelines. It provides customizable, ready-to-use environments that cater to collaborative data teams, ensuring that both data scientists and IT departments can work effectively together. Recognized as the #1 Rated ML Platform, it focuses on bridging technical needs with operational requirements. The platform offers high-performance notebooks with substantial resources, including up to 4TB of RAM and GPU support for accelerated training. It integrates seamlessly with Dask for parallel processing and allows users to create and manage end-to-end data pipelines in the cloud. Saturn Cloud also provides various pricing plans to accommodate different usage needs, making it a versatile choice for data scientists and IT departments alike.

Spell is a New York-based technology company founded in 2017 that provides an MLOps platform for deep learning. The platform is designed to streamline machine learning and deep learning projects for developers and enterprises. In June 2022, Spell was acquired by Reddit to enhance its capabilities in AI and machine learning infrastructure. Spell's core mission is to make deep learning and artificial intelligence accessible to everyone. The company offers a SaaS-based AI infrastructure platform that manages the full lifecycle of deep learning models, including preparation, training, deployment, and management. Key features include infrastructure orchestration, experimentation tools, a development environment with Jupyter notebooks, model operations, and framework flexibility. Spell serves B2B SaaS clients in the High Tech market, helping organizations operationalize AI for various use cases such as natural language processing, computer vision, and speech recognition.

Valohai is a cloud-agnostic MLOps platform based in Turku, Finland, founded in 2016. It is designed for machine learning pioneers and data science teams, helping them automate, manage, and scale their machine learning workflows from feature extraction to model deployment. Valohai enables teams to run experiments across various cloud providers and on-premise infrastructure, ensuring flexibility and avoiding vendor lock-in. The platform offers a range of capabilities, including automated workflow management, version control for data, code, and models, and experiment tracking. It supports popular deep learning frameworks like TensorFlow and Keras, facilitating collaboration among ML teams. Valohai also provides private installation support, prioritizing the security of intellectual property. The company serves enterprise teams in regulated industries such as healthcare, finance, and life sciences, with a global presence across Europe, Asia, and the USA. In November 2023, Valohai was acquired by Weights & Biases, enhancing its reach in the machine learning space.

ZenML is an open-source MLOps framework that simplifies the creation of portable, production-ready machine learning pipelines. Founded in 2021 and based in Munich, Germany, the company aims to accelerate problem-solving by enabling data scientists to automate their workflows from the start. With a dynamic, globally distributed team, ZenML offers both remote work options and office access. The platform serves as an AI control plane and vendor-agnostic orchestration layer, standardizing machine learning workflows. It supports a unified toolkit for various machine learning models and workflows, ensuring seamless transitions across cloud providers like AWS, GCP, and Azure. ZenML provides end-to-end pipelines, governance, and versioning capabilities, along with a managed enterprise version called ZenML Pro, which includes enhanced security and compliance features. The company targets data scientists, ML engineers, and AI teams, particularly in regulated industries such as finance and healthcare.