Executive Guide to Deploying, Monitoring, and Maintaining Models

Explore the MLOps portion of deploying, monitoring, and maintaining models for ML projects.

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?