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    1. Home
    2. Machine Learning & Ai
    3. Awesome Production Machine Learning

    Awesome Production Machine Learning

    A curated list of awesome open source libraries to deploy, monitor, version, and scale machine learning systems in production. Focuses on MLOps, model serving, and production-ready ML tools.

    Overview

    Awesome Production Machine Learning provides resources for deploying, monitoring, versioning, and scaling machine learning models in production environments.

    Features

    • Model Serving - Tools for deploying ML models to production
    • MLOps Platforms - End-to-end machine learning operations platforms
    • Model Monitoring - Tools for monitoring model performance and drift
    • Version Control - Systems for versioning models and data
    • Feature Stores - Platforms for managing and serving features
    • Experiment Tracking - Tools for tracking ML experiments
    • Model Optimization - Libraries for optimizing models for production

    Categories Covered

    • Deployment and serving (TensorFlow Serving, TorchServe, Seldon)
    • Model monitoring and observability
    • Feature engineering pipelines
    • Data versioning and lineage
    • A/B testing frameworks
    • Model explainability tools

    Popular Tools

    • MLflow for experiment tracking
    • Kubeflow for ML workflows on Kubernetes
    • Seldon for model deployment
    • Great Expectations for data validation
    • Feast for feature stores

    Use Cases

    • Deploying ML models at scale
    • Monitoring model performance in production
    • Managing ML workflows
    • Ensuring model reproducibility
    • Implementing CI/CD for ML

    Pricing

    Free and open-source library collection.

    Surveys

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    Information

    Websitegithub.com
    PublishedMar 24, 2026

    Categories

    1 Item
    Machine Learning & Ai

    Tags

    3 Items
    #mlops#production#machine-learning

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