Sharing some interesting (preliminary) results for the 2024 State of Production ML Survey:

    Deploying ML: 36% take 1-3 months, 21% 3-6 months

    Experiment Tracking: 42% use MLFlow, 10% Spreadsheets

    Feature Stores: 53% use none, 28% Custom-built

    Vector DB: 55& use none, rest unconsolidated

    Training: 27& use custom-built, 21% Databricks

    Serving: 56% use custom-built (+ FastAPI/Flask)

    Monitoring: 50% use none, 24% Custom-built

    Diversity: Only 4% identify as female

This is a community initiative and the data will provide all of us in the community with actionable insights to improve the ecosystem. We aim for this input will help create a comprehensive overview of common practices, tooling preferences, and challenges faced when deploying models to production, ultimately benefiting the entire ML community

We are opening this survey until the end of October, and we'll publish the results for the community to derive useful insights! If you can please take two minutes to share your experience: https://bit.ly/state-of-ml-2024

You can also check out the preliminary results here: https://ethical.institute/state-of-ml-2024 - we are building an interface for basic slice and dice to enable extracting further insights (but still early WIP so feedback appreciated). Final results / report will be published end of October!