AIF-C01 ML Lifecycle, MLOps and Evaluation Guide

The ML lifecycle matters on AIF-C01 because AWS wants candidates to know that useful ML is more than model training. Problem framing, data preparation, training, validation, deployment, monitoring, and retraining all belong in the lifecycle.

MLOps: Practices that help operationalize machine learning through repeatable deployment, monitoring, governance, and lifecycle management.

Drift: Change over time in data or conditions that can weaken model performance after deployment.

Lifecycle map

1. define the problem and success metric
2. collect and prepare data
3. train and validate the model
4. deploy for inference
5. monitor drift, quality, and outcomes
6. retrain or adjust when needed

Why each stage matters

Why MLOps matters

MLOps exists because models can degrade over time, data can shift, and production deployment needs repeatability. If an answer treats model training as the entire story, it is usually too narrow.

What the exam is really testing

This lesson is mostly about operational realism:

• a working pilot is not the same thing as a healthy production lifecycle
• evaluation happens before and after deployment
• monitoring matters because data and behavior can shift
• repeatability and governance matter once ML becomes operational

Common traps

• treating training as the entire ML story
• forgetting that monitoring and retraining belong after deployment
• assuming evaluation is a one-time checkpoint instead of part of an ongoing loop
• ignoring data quality while focusing only on model choice

Harder scenario question

A model performed well during validation, but three months after launch its predictions are getting worse because customer behavior changed. Which lifecycle instinct is strongest first?

• A. Ignore the issue because the model already passed validation
• B. Monitor the drift, evaluate the new behavior, and retrain or adjust as needed
• C. Delete all metrics
• D. Move the model to colder storage

Correct answer: B. AIF-C01 expects you to treat deployment as part of a monitored lifecycle, not the finish line.

Decision order that usually wins

1. Classify the step as data preparation, training, evaluation, deployment, or monitoring.
2. Follow the lifecycle order before choosing a fix.
3. Use evaluation to judge model quality before deployment.
4. Treat post-deployment monitoring as an ongoing stage, not an optional extra.
5. Keep experimentation, productionization, and monitoring in separate lanes.

Quiz