omega-ml
  • Introduction
  • User Guide
    • Getting started
    • Generative AI
    • Classic ML
      • Tutorial for classic ML
        • Loading datasets for training
        • Training models
        • Evaluating models
        • Deploying models
        • Adding business logic to models
        • Serving models for real-time predictions
        • Tracking and monitoring models
      • Working with models
        • Introduction to models
        • Capturing model metrics
        • Adding business logic to models
        • Working with ML frameworks
        • Monitoring model and data drift
      • Working with datasets
    • Task Automation
    • Deployment and Operations
    • Command-line interface
    • Advanced features
  • Deploying omega-ml
  • Extending omega-ml
  • Reference
  • Support
  • Changes
omega-ml
  • User Guide
  • Classic ML
  • Tutorial for classic ML
  • Working with models
  • Working with ML frameworks
  • View page source

Working with ML frameworksΒΆ

  • Tensorflow
    • Concepts
    • Keras models
    • tf.data.Dataset
  • MLFlow
    • Using mlflow saved models
    • Using a MLModel file
    • Using a mlflow Model or PythonModel
    • Serving mlflow model runs
    • Model frameworks supported via mlflow
    • Disclaimer and License
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