Introduction to Continuous Integration/Continuous Delivery for Machine Learning

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CI/CD for Machine Learning

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In this chapter, you will explore the essential principles of Continuous Integration/Continuous Delivery (CI/CD) and YAML. You'll grasp the software development life cycle and key terms like build, test, and deploy. Discover the differences between Continuous Integration, Continuous Delivery, and Continuous Deployment. Moreover, you'll investigate the significance of CI/CD in machine learning and experimentation.

Exercise 1: Introduction to Continuous Integration/Continuous Delivery for Machine Learning

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Exercise 2: Continuous deployment and delivery Exercise 3: Machine learning workflow Exercise 4: Introduction to YAML Exercise 5: YAML syntax Exercise 6: YAML mappings and sequences Exercise 7: Introduction to GitHub Actions Exercise 8: Utility of GitHub Actions Exercise 9: Anatomy of GitHub Actions

Get ready to explore GitHub Actions (GHA), an influential platform for executing CI/CD workflows. Uncover the diverse components of GHA, encompassing events, actions, jobs, steps, runners, and context. Gain insights into crafting workflows that activate upon events like push and pull requests, and tailor runner machines. Dive into hands-on learning as you establish fundamental CI pipelines and grasp the intricacies of the GHA log.

Exercise 1: Intermediate YAML Exercise 2: Find the correct combination Exercise 3: Design a Continuous Integration workflow Exercise 4: Setting a basic CI pipeline Exercise 5: Interpret GitHub Actions Workflow Exercise 6: Write a GitHub Actions Workflow Exercise 7: Running repository code Exercise 8: Feature branches in shared repository model Exercise 9: Running Python code in GitHub Actions Exercise 10: Environment Variables and Secrets Exercise 11: What is GITHUB_TOKEN?Exercise 12: Working with environment variables Exercise 13: Working with secrets

In this chapter, you'll explore the integration of machine learning model training into a GitHub Action pipeline using Continuous Machine Learning GitHub Action. You'll generate a comprehensive markdown report including model metrics and plots. You will also delve into data versioning in Machine Learning by adopting Data Version Control (DVC) to track data changes. The chapter also covers setting DVC remotes and dataset transfers. Finally, you'll explore DVC pipelines, configuring a DVC YAML file to orchestrate reproducible model training.

Exercise 1: Model training with GitHub Actions Exercise 2: Develop a classification model Exercise 3: Train a classification model Exercise 4: Setup model training using CML Exercise 5: Versioning datasets with Data Version Control Exercise 6: Why are .dvc files needed?Exercise 7: Data versioning in action Exercise 8: Interacting with DVC remotes Exercise 9: Exploring the Benefits of DVC Remotes Exercise 10: DVC remotes in action Exercise 11: DVC Pipelines Exercise 12: Creating a DVC pipeline Exercise 13: Train ML models with DVC

In this chapter, you will direct your attention towards the analysis of model performance and the fine-tuning of hyperparameters. You will acquire practical expertise in comparing metrics and visualizations across different branches to assess changes in model performance. You will conduct hyperparameter tuning using scikit-learn's GridSearchCV. Furthermore, you will delve into the automation of pull requests using the optimal model configuration.

Exercise 1: Comparing metrics and plots in DVC Exercise 2: Adding metrics and plots to dvc.yaml Exercise 3: Comparing metrics across Git branches Exercise 4: Run DVC pipeline in GitHub Actions Exercise 5: Hyperparameter Tuning with DVC Exercise 6: Adding Hyperparameter tuning to dvc.yaml Exercise 7: Running Hyperparameter tuning DVC pipelines Exercise 8: GitHub Actions workflow for Hyperparameter Tuning Exercise 9: Loose Coupling Exercise 10: Setup Hyperparameter Tuning in GitHub Actions Exercise 11: Congratulations!