MLOps (Machine Learning Operations) Fundamentals

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  • Welcome to MLOps Fundamentals
    • This module provides the overview of the course
  • Why and When do we need MLOps
    • In this module, we take a look at machine learning from an operations perspective. This means taking a whole-system view: from defining the problem to the solution.
  • Understanding the Main Kubernetes Components (Optional)
  • Introduction to AI Platform Pipelines
    • In this module, we’ll be discussing a Google Cloud product, AI Platform Pipelines, that makes MLOps easy, seamless, and scalable with Google Cloud Services.
  • Training, Tuning and Serving on AI Platform
    • In this module, we will learn how to train, tune, and serve a model manually from the Jupyter notebook on AI Platform.
  • Kubeflow Pipelines on AI Platform
    • In this module, we will automate the training and tuning process we described before using a Kubeflow pipeline. Instead of having to trigger every single step of the process manually from the Jupyterlab notebook, we can trigger the entire process with a single click after we have expressed the various steps as a Kubeflow pipeline.
  • CI/CD for Kubeflow Pipelines on AI Platform
    • In this module, we will be talking about CI/CD for Kubeflow pipelines. We know how to build an automated Kubeflow pipeline, but how can we integrate this pipeline in a continuous integration stack? The goal is to rebuild pipeline assets immediately when new training code is pushed to the corresponding repository.
  • Summary
    • This module is a recap of what was covered in the course

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