Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.
MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
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Overview
Syllabus
Week 1 – Drift and Feedback Loops
- Module 1: Training Versus Inference Pipelines
- Module 2: Drift & Feedback Loops
Week 2 – Triggers, Alarms & Model Stability
- Module 3: Triggers & Alarms
- Module 4: Model Stability
Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)
- Module 5: CI/CD
Week 4 – Model Security and Responsible AI
- Module 6: Responsible AI
Taught by
Peter Bruce, Evan Wimpey, Vic Diloreto, Laura Lancheros, Greg Carmean, Kuber Deokar and Janet Dobbins
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