[D] 5 considerations for Deploying Machine Learning Models in Production – what did I miss?

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

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  • evidently

    Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

    Consideration Number #5: For model observability look to Evidently.ai, Arize.ai, Arthur.ai, Fiddler.ai, Valohai.com, or whylabs.ai.

  • MLflow

    Open source platform for the machine learning lifecycle

    Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • dvc

    🦉 ML Experiments and Data Management with Git

    Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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