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LinkedIn Learning

Executive Guide to Deploying, Monitoring, and Maintaining Models

via LinkedIn Learning

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Overview

Syllabus

1. The Phases of a Machine Learning Project
  • Data and supervised machine learning
  • Data engineering and MLOps in the ML lifecycle
  • Why ML projects fail to be deployed
  • The basics of ML modeling
2. Model Evaluation
  • The business evaluation phase
  • A deployment checklist
3. Scoring
  • Scoring traditional ML models
  • Scoring a "black box" model
  • Scoring an ensemble
4. Deployment
  • Batch vs. real-time scoring
  • Data prep and scoring
  • Combining batch and real-time scoring
5. Monitoring and Maintenance
  • What is model monitoring?
  • How often should you rebuild?

Taught by

Keith McCormick

Reviews

4.6 rating at LinkedIn Learning based on 40 ratings

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