ML Engineering for DevOps/SRE Professionals

Welcome to the ML Engineering Syllabus designed specifically for professionals with DevOps and SRE backgrounds. This curriculum bridges your existing infrastructure and operations expertise with modern machine learning engineering practices.

Why This Syllabus?

As a DevOps/SRE professional, you already possess critical skills that translate directly to ML Engineering:

  • Infrastructure as Code expertise
  • CI/CD pipeline design and management
  • Monitoring and observability practices
  • System reliability and performance optimization
  • Container orchestration and deployment strategies

This syllabus builds upon these foundations to help you master ML-specific challenges.

Learning Path

Module 1: Fundamentals
Core ML concepts and Python essentials for ML engineering
Module 2: MLOps
Operationalizing ML with DevOps principles
Module 3: Infrastructure
ML-specific infrastructure and deployment patterns

Prerequisites

  • Strong understanding of Linux systems
  • Experience with containerization (Docker/Kubernetes)
  • Familiarity with CI/CD concepts
  • Basic programming knowledge (any language)
  • Cloud platform experience (AWS/GCP/Azure)

Timeline

This curriculum is designed to be completed in 6-8 months with approximately 10-15 hours per week of study and practice.

Getting Started

  1. Begin with the Fundamentals module
  2. Set up your development environment
  3. Join our community discussions
  4. Track your progress through hands-on projects