About This Course

This second ML course focuses on practical framework implementation and production system design. While Course 1 covers theory and algorithms, this course emphasizes building scalable, maintainable ML systems that solve real business problems. Content is being actively developed and uploaded - check back regularly for new materials.

Core Curriculum

Framework Modules

Four comprehensive frameworks covering the ML system lifecycle from design to production

FRAMEWORK 1

ML System Architecture

Designing Scalable ML Systems

Learn to design ML systems that scale with organizational needs, covering architectural patterns, component design, and system integration strategies.

  • Microservices vs monolithic ML architectures
  • Feature stores and data management patterns
  • Model registry and versioning systems
  • API design for ML services
Architecture Production
FRAMEWORK 2

ML Pipeline Engineering

End-to-End Pipeline Development

Build robust ML pipelines from data ingestion through model serving, with emphasis on automation, monitoring, and reproducibility.

  • Data pipeline orchestration (Airflow, Prefect)
  • Feature engineering pipelines
  • Training pipeline automation
  • Continuous training systems
Code Production
FRAMEWORK 3

Model Deployment & MLOps

Production Model Management

Master MLOps practices for deploying, monitoring, and maintaining ML models in production environments.

  • Containerization (Docker, Kubernetes)
  • Model serving patterns (batch, real-time, streaming)
  • A/B testing and canary deployments
  • Model monitoring and drift detection
Production Advanced
FRAMEWORK 4

Enterprise ML Integration

Business System Integration

Integrate ML capabilities with enterprise systems, covering data governance, security, and organizational change management.

  • Enterprise data architecture integration
  • Security and compliance frameworks
  • Cost optimization and resource management
  • ML team organization and workflows
Architecture Advanced

Development Roadmap

Track content development progress - materials uploaded as completed

1

Framework 1: ML System Architecture

Core architectural patterns and system design principles for ML applications

Uploading
2

Framework 2: Pipeline Engineering

Data and training pipeline construction with orchestration tools

Planned
3

Framework 3: MLOps & Deployment

Production deployment, monitoring, and continuous improvement

Planned
4

Framework 4: Enterprise Integration

Business system integration and organizational deployment

Planned