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MLOps Essentials: Model Deployment and Monitoring

via LinkedIn Learning

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Details

Provider

LinkedIn Learning
Pricing

Free Trial Available
Languages

English
Certificate

Certificate Available
Effort

1 hour 24 minutes
Sessions

Self-Paced
Level

Intermediate

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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 312 ratings

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