Executive Guide to Deploying, Monitoring, and Maintaining Models
Offered By: LinkedIn Learning
Course Description
Overview
Explore the MLOps portion of deploying, monitoring, and maintaining models for ML projects.
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
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent