• Contents:

Phases of a ML Project

Ref: https://www.udemy.com/course/aws-ai-practitioner-certified/learn/lecture/44886663

Image source: https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/well-architected-machine-learning-lifecycle.html

MLOps

Ref: https://www.udemy.com/course/aws-ai-practitioner-certified/learn/lecture/45375491

• 💡 Extension of DevOps (→ deploy code regularly)
• 🔧 Make sure ML models aren’t just developed but also deployed, monitored, retrained systematically and repeatedly
• Key Principles:
  • Version control: data, code, models could be rolled back if necessary
  • Automation: of all stages, including data ingestion, pre-processing, training, etc…
  • Continuous Integration: test models consistently
  • Continuous Delivery: of model in productions
  • Continuous Retraining
  • Continuous Monitoring
• Example MLOps architecture