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Feast (Feature Store) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference. Feast allows ML platform teams to: Make features consistently available for training and serving by managing an offline store (to process historical data for scale-out batch scoring or model training), a low-latency online store (to power real-time prediction), and a battle-tested feature server (to serve pre-computed features online). Avoid data leakage by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset-joining logic. This ensures that future feature values do not leak to models during training. Separate machine learning from data infrastructure by creating a single data access layer that hides how features are stored from how they are retrieved, making sure models can easily adapt when switching from training to serving, from batch processing to real-time processing, and between different data systems.
Metaflow is a human-centric framework designed to help scientists and engineers build and manage real-life AI and ML systems. Serving teams of all sizes and scales, Metaflow streamlines the entire development lifecycle—from rapid prototyping in notebooks to reliable, maintainable production deployments—enabling teams to iterate quickly and deliver robust systems efficiently. Originally developed at Netflix and now supported by Outerbounds, Metaflow is designed to boost productivity for research and engineering teams working on a wide variety of projects, from classical statistics to state-of-the-art deep learning and foundation models. By unifying code, data, and compute at every stage, Metaflow ensures seamless, end-to-end management of real-world AI and ML systems. Today, Metaflow powers thousands of AI and ML experiences across a diverse array of companies, large and small, including Amazon, Doordash, Dyson, Goldman Sachs, Ramp, and many others. At Netflix, Metaflow helps with more than 3000 AI and ML projects, runs hundreds of millions of complex computing tasks that handle huge amounts of data, and keeps track of tens of petabytes of models and resources for many users in its AI, ML, data science, and engineering teams.