Machine Learning Engineering

• Design and build reliable feature and training pipelines
• Deploy models as monitored services
• Apply MLOps practice: versioning, testing, reproducibility, rollback
• Work with deep learning frameworks on realistic problems
• Own an ML system end to end

Curriculum

1. 01

   Engineering foundations for ML

   • Software design for ML
   • Testing
   • Containers
   • Cloud fundamentals
2. 02

   Data and feature pipelines

   • Batch and streaming data
   • Feature stores
   • Orchestration
   • Data validation
3. 03

   Deep learning and advanced modelling

   • Neural networks
   • Training at scale
   • Transfer learning
   • Evaluation
4. 04

   Production ML

   • Model serving
   • Monitoring and drift
   • CI/CD for models
   • Capstone system

Projects you will build

• End-to-end recommendation service with monitoring
• Automated retraining pipeline
• Deployed deep learning model with an API and dashboard