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. Each framework includes architectural patterns,
hands-on implementation guides, and interactive examples.
Framework Modules
Framework 1
ML System Architecture
Designing Scalable ML Systems
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
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1ML System ArchitectureCore architectural patterns and system design principles for ML applicationsLive
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2Pipeline EngineeringData and training pipeline construction with orchestration toolsLive
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3MLOps & DeploymentProduction deployment, monitoring, and continuous improvementLive
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4Enterprise IntegrationBusiness system integration and organizational deploymentLive