Transform Your DevOps Skills into MLOps Expertise

This hands-on bootcamp is the ultimate training ground for DevOps Engineers and infrastructure professionals eager to transition into the exciting field of MLOps. As machine learning continues to revolutionize the technology landscape, MLOps serves as the vital connection between machine learning models and their deployment in production environments. In this comprehensive course, participants will work on a real-world regression use case—predicting house prices—guiding them from initial data processing to full-scale production deployment on Kubernetes.

Throughout the course, you will establish your environment using Docker and MLFlow for efficient experiment tracking. Gain a solid understanding of the machine learning lifecycle, as you delve into data engineering, feature engineering, and model experimentation with Jupyter notebooks. As you progress, you’ll learn to package your models with FastAPI and create a user-friendly interface using Streamlit, enabling seamless model interaction. Furthermore, you will automate your ML workflows using GitHub Actions, ensuring a streamlined CI process as you push Docker containers to DockerHub.

In the latter part of the bootcamp, you will design a scalable inference infrastructure on Kubernetes, exposing various services and linking frontends with backends through service discovery. You’ll also explore production-grade model serving with Seldon Core and monitor your deployments with insightful Grafana and Prometheus dashboards. By the end of this course, you’ll have the knowledge and practical experience necessary to excel in MLOps and AI Platform Engineering roles, fully equipped to automate and administer complex machine learning workflows using modern DevOps practices.

What you will learn:

• Build end-to-end Machine Learning pipelines with MLOps best practices
• Understand and implement ML lifecycle from data engineering to model deployment
• Set up MLFlow for experiment tracking and model versioning
• Package and serve models using FastAPI and Docker
• Automate workflows using GitHub Actions for CI pipelines
• Deploy inference infrastructure on Kubernetes using KIND
• Use Streamlit for building lightweight ML web interfaces
• Learn GitOps-based CD pipelines using ArgoCD
• Serve models in production using Seldon Core
• Monitor models with Prometheus and Grafana for production insights
• Understand handoff workflows between Data Science, ML Engineering, and DevOps
• Build foundational skills to transition from DevOps to MLOps roles

Course Content:

• Sections: 10
• Lectures: 40
• Duration: 12 hours

Requirements:

• Basic knowledge of DevOps and Docker
• Familiarity with Git and GitHub
• Some exposure to Python (used for scripting and ML workflows)
• Prior understanding of CI/CD concepts is helpful but not mandatory
• A machine with minimum 8GB RAM and Docker installed for running local labs

Who is it for?

• DevOps Engineers looking to break into the field of MLOps
• Platform Engineers and SREs supporting ML teams
• Cloud Engineers wanting to understand ML workflows and productionization
• Developers transitioning into ML Engineering or Data Engineering roles
• Anyone curious about how real-world ML systems are deployed and scaled

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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