This MLOps tutorial series by DVCorg introduces modern best practices for building reliable and production-ready machine learning systems. It focuses on applying software engineering principles such as CI/CD, version control, and automated testing to machine learning workflows.
The course begins with an introduction to continuous integration for machine learning, explaining how ML systems can be structured similarly to traditional software projects. You will learn why reproducibility and automation are critical in ML development.
A major topic covered is handling large datasets that cannot fit into Git, and how to manage data efficiently using modern versioning approaches. The series also explains how to track machine learning models using Git and GitHub Actions, enabling better collaboration and experiment management.
You will explore how to automate workflows using GitHub Actions, including running ML pipelines on custom hardware such as GPUs. The course also covers automated testing strategies for machine learning models, ensuring model reliability and performance consistency.
Advanced sections include behavioral testing for ML models and integrating CI/CD pipelines into real-world data science projects. There is also a discussion on testing strategies with industry experts, helping you understand practical challenges in production ML systems.
By the end of this series, you will understand how to build scalable, testable, and automated machine learning systems using MLOps principles and modern DevOps tools.
