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LinkedIn Learning

MLOps Essentials: Model Deployment and Monitoring

via LinkedIn Learning

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Overview

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Learn how to deploy and monitor machine learning models to deliver scalable, reliable ML products and services.

Syllabus

Introduction
  • Getting started with MLOps
  • Course coverage
  • Review of MLOps lifecycle
1. Continuous Delivery
  • An ML production setup
  • Deployment pipelines
  • Deployment rollout strategies
  • Planning for infrastructure
  • Deployment best practices
  • Tools and technologies for deployment
2. Model Serving
  • Model serving patterns
  • Scaling model serving
  • Building resiliency in serving
  • Serving multiple models
  • Tools and technologies for serving
3. Continuous Monitoring
  • The monitoring pipeline
  • Instrumentation for observability
  • Metrics to monitor
  • ML production data best practices
  • Alerts and thresholds for ML
  • Tools and technologies for monitoring
4. Drift Management
  • Introduction to model drift
  • Concept drift basics
  • Managing concept drift
  • Feature drift basics
  • Managing feature drift
5. Responsible AI
  • Elements of responsible AI
  • Explainable AI
  • Fairness in ML
  • Security of ML assets
  • Privacy in machine learning
Conclusion
  • Continuing on with MLOps

Taught by

Kumaran Ponnambalam

Reviews

4.5 rating at LinkedIn Learning based on 165 ratings

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