There are in total 6 parts, but the following two are more interested to me:

• End-to-End ML Workflow Lifecycle. This article gives you the overall implementation components of a end-to-end machien learning workflow. It introduces:

  • What is data engineering and how this fit into the workflow
  • What is model engineering and how this fit into the workflow
  • what is model deployment and how this fit into the workflow.

• MLOps Principles. Only knowing the components are not enough. In order to follow the best practice of MLOps, this article illustrates what are the principles we should follow so that we can build a robust, reliable ML product with less possibily of useless effort, including:

  • Automation
  • Continuous X (continuous integration, Continuous delivery, continuous training and continuousmonitoring)
  • Versioning
  • Experiments Tracking
  • Testing
  • Monitoring
  • "ML Test Score" System
  • Reproducibility
  • Loosely Coupled Architecture (Modularity)
  • ML-based Software Delivery Metrics (4 metrics from “Accelerate”)