- Production Monitoring Foundations
-
Concept | Process governance for MLOps
-
Concept | Model comparisons
-
Concept | Model evaluation stores
-
Concept | Monitoring model performance and drift in production
- Model Monitoring Basics
-
Get started
-
Create a model monitoring pipeline for each approach
-
Compare and contrast model monitoring pipelines
-
Run more model evaluations
-
Conduct drift analysis
-
Automate model monitoring
-
Create additional model monitoring assets
- Model Monitoring Feedback Loops
-
Concept | Monitoring and feedback in the AI project lifecycle
-
Tutorial | API endpoint monitoring basics
- Optional: Production Deployment Automation
-
Get started
-
Start with a retrain model scenario
-
Add a Create bundle or API service version step
-
Add an Update project or API deployment step
-
Run the scenario & observe the outcome
-
Plan for a more robust setup
- Optional: Model Monitoring in Different Contexts
-
Get started
-
A batch workflow within Dataiku
-
An API endpoint on a Dataiku API node
-
An exported Python model scored externally
-
An exported Java model scored externally
-
What's next?
- Wrap Up
-
Course checkpoint | Production Monitoring
-
Tell us what you think | Production Monitoring
-
Course complete | Production Monitoring
![](https://cc.sj-cdn.net/instructor/1okd5xripib5k-dataiku/courses/ybyd37stvujj/promo-image.1699567288.png)
Production Monitoring
Learn how to monitor deployed projects and API services in production.