Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
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, and performing hyperparameter tuning.
You'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.
You'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.
You'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.
You'll then focus on model performance analysis and hyperparameter tuning and gain practical skills in comparing metrics and plots across branches to track changes in model performance. You'll learn how to perform hyperparameter tuning using scikit-learn's GridSearchCV. Additionally, you'll explore automating pull requests with the best model configuration.
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, and performing hyperparameter tuning.
You'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.
You'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.
You'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.
You'll then focus on model performance analysis and hyperparameter tuning and gain practical skills in comparing metrics and plots across branches to track changes in model performance. You'll learn how to perform hyperparameter tuning using scikit-learn's GridSearchCV. Additionally, you'll explore automating pull requests with the best model configuration.
