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MLOps Mastery: Build Production-Ready AI Systems
Week 1: ML Engineering Foundations
Foundations of machine learning engineering
Software engineering best practices for ML
Project organization and clean code principles
Saturday Lab: Building a reproducible ML project structure
Week 2: Data Engineering for ML
Data pipelines and processing at scale
Data version control with DVC
Feature stores and feature engineering
Saturday Lab: Implementing a production-grade feature store
Week 3: Model Development Lifecycle
Model development workflows
Reproducible training environments
Model validation and verification
Saturday Lab: Creating a reproducible model training pipeline
Week 4: CI/CD for Machine Learning
Continuous integration for ML code and data
Continuous delivery of ML models
Testing strategies for ML systems
Saturday Lab: Building an end-to-end MLOps pipeline
Week 5: Model Deployment
Deployment strategies and patterns
Designing robust APIs for ML services
Scalable inference infrastructure
Saturday Lab: Implementing scalable model serving
Week 6: Production Monitoring
Monitoring ML system performance
Detecting and handling model drift
Continuous model improvement
Saturday Lab: Building a comprehensive monitoring system
Week 7: Advanced MLOps
Security, privacy, and compliance
Cost optimization for ML infrastructure
Advanced MLOps architectures
Saturday Lab: Capstone project planning and design
Week 8: Capstone Project
Implementation of complete MLOps system
Testing and deployment
Project presentations and peer review
Saturday: Final presentation and course completion
Data pipelines and processing at scale
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