DevOps to MLOps Bootcamp: Build & Deploy ML Systems End-to-End
Unlock DevOps and MLOps skills to build, scale, and deploy AI systems.
Created by Gourav Shah
Discover how to bridge the gap between DevOps and MLOps by building and deploying real machine learning systems. You will learn to automate workflows, manage experiments, and scale models using industry-standard tools and practices. Gain practical skills to take ML projects from development to production.
Packt | May 2025 | 501 min
What You Will Learn
You will work through hands-on projects that guide you from setting up your environment to deploying and scaling machine learning models. By following practical coding examples and building real applications, you will gain confidence in managing the full MLOps lifecycle, including automation, monitoring, and continuous delivery.
Key Features
- Set up and automate ML workflows using Docker, Kubernetes, and GitHub Actions
- Containerize and deploy machine learning models with FastAPI for scalable inference
- Monitor, scale, and manage ML systems using Prometheus, Grafana, and GitOps tools
Target Audience
Designed for data scientists, ML engineers, and DevOps professionals who want to implement robust MLOps pipelines. If you have a working knowledge of machine learning, Python, and Git, and are looking to scale and automate ML deployments, this course will help you reach your goals.
