The course will empower you to streamline your machine learning development processes, enhancing efficiency, reliability, and reproducibility in your projects. Throughout the course, you’ll develop a comprehensive understanding of CI/CD workflows and YAML syntax, utilizing GitHub Actions (GA) for automation, training models in a pipeline, versioning datasets with DVC, performing hyperparameter tuning, and automating testing and pull requests.Fundamentals of CI/CD, YAML, and Machine LearningYou’ll be introduced to the fundamental concepts of CI/CD and YAML, and gain an understanding of the software development life cycle and key terms like build, test, and deploy. You’ll define Continuous Integration, Continuous Delivery, and Continuous Deployment while examining their distinctions. You’ll also explore the utility of CI/CD in machine learning and experimentation.GitHub Actions for CI/CD AutomationYou’ll learn about GA, a powerful platform for implementing CI/CD workflows. You’ll discover the various elements of GA, including events, actions, jobs, steps, runners, and context. You’ll learn how to define workflows triggered by events such as push and pull requests and customize runner machines. You’ll also gain practical experience by setting up basic CI pipelines and understanding the GA log.Versioning Datasets with Data Version ControlYou’ll delve deep into Data Version Control (DVC) for versioning datasets, initializing DVC, and tracking datasets. Using DVC pipelines, you’ll learn how to train classification models and generate metrics in a reproducible manner.Optimizing Model Performance and Hyperparameter TuningYou’ll now focus on model performance analysis and hyperparameter tuning and gain practical skills in diffing metrics and plots across branches to compare changes in model performance. You’ll learn how to download artifacts using GA and perform hyperparameter tuning using scikit-learn’s GridSearchCV. Additionally, you’ll explore automating pull requests with the best model configuration.