Deploy and Monitor a Complete Prediction Pipeline
A model that worked yesterday can fail today even when its code has not changed.
Deploy and Monitor a Complete Prediction Pipeline explained visually
A model that worked yesterday can fail today even when its code has not changed. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
A model that worked yesterday can fail today even when its code has not changed.
Save more than a model
A model that worked yesterday can fail today even when its code has not changed. The incoming data may have changed, a feature may be missing, or preprocessing may differ from training. Deployment means preserving the whole prediction contract: inputs, transformations, model version, output meaning, and the decisions built around it.
Watch input and output behavior
Package the preprocessing state and feature order with the model. Validate input types and ranges, measure latency, and test representative requests. Keep a known-good version available for rollback. A model update should be evaluated before activation, especially when it changes thresholds or the meaning of output scores. Versioned artifacts help explain which system produced a particular result.
Update with evidence
Monitoring can track missing features, unusual input distributions, prediction distributions, response times, and observed errors when labels become available. Distribution change is a signal to investigate, not proof that accuracy has declined. Likewise, a stable average can hide a failing subgroup. Define a review and retraining process using new, trustworthy labels. Saving additional data alone does not train or improve the model automatically.
A worked example
If training used features ordered as distance, temperature, and traffic, a deployment request ordered as traffic, distance, and temperature can produce plausible-looking but incorrect predictions. Enforcing named features and a fixed preprocessing pipeline prevents this silent mismatch.
Worked example
If training used features ordered as distance, temperature, and traffic, a deployment request ordered as traffic, distance, and temperature can produce plausible-looking but incorrect predictions. Enforcing named features and a fixed preprocessing pipeline prevents this silent mismatch.
Quick check: test your understanding
Try it yourself: Write three checks to perform before replacing a production model.
Check the reasoning
Compare representative held-out performance, verify input/output compatibility, and confirm latency, resource use, and rollback behavior.
Does detecting data drift automatically prove worse prediction accuracy?
No. Drift indicates a change that needs investigation and, when possible, labeled evaluation.
How would you solve the practice case?
Compare representative held-out performance, verify input/output compatibility, and confirm latency, resource use, and rollback behavior.
What does the worked example show?
If training used features ordered as distance, temperature, and traffic, a deployment request ordered as traffic, distance, and temperature can produce plausible-looking but incorrect predictions. Enforcing named features and a fixed preprocessing pipeline prevents this silent mismatch.
Further reading
Machine Learning — Tom M. Mitchell.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 15 transcript208 words · Video transcript · Matches the published narration and captionsOpen transcript
A model that worked yesterday can fail today even when its code has not changed. The incoming data may have changed, a feature may be missing, or preprocessing may differ from training. Deployment means preserving the whole prediction contract: inputs, transformations, model version, output meaning, and the decisions built around it.
Package the preprocessing state and feature order with the model. Validate input types and ranges, measure latency, and test representative requests. Keep a known-good version available for rollback. A model update should be evaluated before activation, especially when it changes thresholds or the meaning of output scores. Versioned artifacts help explain which system produced a particular result.
Monitoring can track missing features, unusual input distributions, prediction distributions, response times, and observed errors when labels become available. Distribution change is a signal to investigate, not proof that accuracy has declined. Likewise, a stable average can hide a failing subgroup. Define a review and retraining process using new, trustworthy labels. Saving additional data alone does not train or improve the model automatically.
If training used features ordered as distance, temperature, and traffic, a deployment request ordered as traffic, distance, and temperature can produce plausible-looking but incorrect predictions. Enforcing named features and a fixed preprocessing pipeline prevents this silent mismatch.