Learn how to deploy and monitor machine learning models to deliver scalable, reliable ML products and services.
Overview
Syllabus
Introduction
- Getting started with MLOps
- Course coverage
- Review of MLOps lifecycle
- An ML production setup
- Deployment pipelines
- Deployment rollout strategies
- Planning for infrastructure
- Deployment best practices
- Tools and technologies for deployment
- Model serving patterns
- Scaling model serving
- Building resiliency in serving
- Serving multiple models
- Tools and technologies for serving
- The monitoring pipeline
- Instrumentation for observability
- Metrics to monitor
- ML production data best practices
- Alerts and thresholds for ML
- Tools and technologies for monitoring
- Introduction to model drift
- Concept drift basics
- Managing concept drift
- Feature drift basics
- Managing feature drift
- Elements of responsible AI
- Explainable AI
- Fairness in ML
- Security of ML assets
- Privacy in machine learning
- Continuing on with MLOps
Taught by
Kumaran Ponnambalam
