Explore the MLOps portion of deploying, monitoring, and maintaining models for ML projects.
Executive Guide to Deploying, Monitoring, and Maintaining Models
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
- The business evaluation phase
- A deployment checklist
- Scoring traditional ML models
- Scoring a "black box" model
- Scoring an ensemble
- Batch vs. real-time scoring
- Data prep and scoring
- Combining batch and real-time scoring
- What is model monitoring?
- How often should you rebuild?
Taught by
Keith McCormick
