MLOps with Data Version Control: CI/CD Using Continuous Machine Learning

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Continuous integration and continuous deployment (CI/CD) are crucial in machine learning operations (MLOps) as they automate the integration of ML models into software development. Continuous machine learning (CML) refers to an ML model's ability to learn continuously from a stream of data. In this course, you will build a complete Data Version Control (DVC) machine learning pipeline in preparation for continuous machine learning. You will modularize your machine learning workflow using DVC pipelines, configure DVC remote storage on Google Drive, and set up authentication for DVC to access Google Drive. Next, you will configure CI/CD through CML and use the open-source CML framework to implement CI/CD within your machine learning project. Finally, you will see how for every git push to your remote repository, a CI/CD pipeline will execute your experiment and generate a CML report with model metrics for every GitHub commit. At the end of this course, you will be able to use DVC’s integration with CML to build CI/CD pipelines.