Udemy – Ultimate DevOps to MLOps Bootcamp – Build ML CI/CD Pipelines 2025-8

During this course, participants will work on a real-world regression use case—house price prediction—and experience all the steps from data processing to deployment to production using Kubernetes. They will start by setting up a development environment using Docker and MLFlow to track experiments. Participants will gain a deep understanding of the machine learning lifecycle and gain hands-on experience in data engineering, feature engineering, and model testing using Jupyter notebooks. In more advanced stages, participants will build a scalable inference infrastructure using Kubernetes, expose services, and connect the frontend and backend using service discovery. They will explore model deployment at the production level using Seldon Core, and monitor their deployments using Prometheus and Grafana dashboards.

What you will learn

• Create integrated machine learning pipelines using MLOps best practices
• Understanding and implementing the machine learning lifecycle from data engineering to model deployment
• Setting up MLFlow to track experiments and model versioning
• Packaging and serving models using FastAPI and Docker
• Automate workflows using GitHub Actions for CI pipelines
• Deploying inference infrastructure on Kubernetes using KIND
• Using Streamlit to build lightweight web interfaces for machine learning
• Learning GitOps-based continuous delivery pipelines using ArgoCD
• Presenting models in an operational environment using Seldon Core
• Monitor models using Prometheus and Grafana for operational insights
• Understand workflows for transferring responsibility between data science, machine learning engineering, and DevOps teams
• Build essential skills to transition from DevOps roles to MLOps

This course is suitable for people who:

• DevOps engineers looking to enter the MLOps field.
• Platform engineers and site reliability engineers (SRE) who support machine learning teams.
• Cloud engineers who want to understand machine learning workflows and operationalize them.
• Developers who are transitioning into machine learning or data engineering roles.
• Anyone curious about how machine learning systems are deployed and scaled in the real world.

Installation Guide

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Subtitles: English

Quality: 720p