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Snorkel builds specialized training datasets and evaluation systems for enterprise AI models, focusing on data quality and expert curation over generic approaches.

Open-source framework and commercial platform for building, observing, and deploying production AI agents.

ZenML provides an open-source Python framework for building, running, and deploying machine learning pipelines across different cloud and on-premises infrastructure.

vLLM is an open-source software library that speeds up and reduces the cost of running large language models (LLMs) for inference, enabling companies to deploy AI chatbots and other generative AI applications more efficiently.

PrivateGPT provides an open-source, self-hosted API for running large language models and performing retrieval-augmented generation on private documents, ensuring data never leaves the user’s local environment.

Polyaxon provides an open-source platform for managing machine learning workflows, including experiment tracking, hyperparameter tuning, and model deployment on Kubernetes.

Pachyderm provides an open-source platform for version-controlling data and automating data pipelines using containers, enabling reproducible data science and machine learning workflows at scale.

MLflow is an open-source platform that helps data scientists and engineers track experiments, package code into reproducible runs, and deploy machine learning models to production.

mLeap serializes machine learning pipelines from Spark, Scikit-learn, and TensorFlow into a portable format and executes them in production without the original training framework.

MindsDB provides a managed platform to run open-source AI agents with built-in model routing, memory, credentials management, and a workspace UI for knowledge workers.