Summary
This chapter emphasized the significance of automation and environment management for building robust and reusable ML workflows. By leveraging ML pipelines, Git, GitHub Actions, and AML environments, you can effectively streamline ML development and ensure consistent, repeatable results across your projects. We learned how to define repeatable and reusable steps for data preparation and training, ensuring consistency and efficiency. We also delved into the importance of managing ML environments to maintain reproducibility and track dependencies. Now, as we move forward, it’s time to focus on another crucial aspect of MLOps: model management. Just as a well-built house requires a strong foundation, a successful ML project needs a robust model management strategy. In Chapter 4, we will explore the core practices of model management, including registration and packaging.
