Welcome to the workshop!
Workshop
In ProgressDetecting Data Drift in Production
Today's icebreaker:
Mind the shift
What might cause a model’s input data to change between training and production?
Share your response in the chat!
Today's agenda:
- **Review:**Recap key concepts.
- **Practice:**Building a data drift alert system.
- **Closing:**Key takeaways and next steps.
Today's learning objectives:
- Reinforce the importance of continuous monitoring and its role in mitigating deployment risks.
- Implement a simple Python script to detect data drift using statistical methods.
- Simulate a production monitoring scenario to visualise how changes in input data distribution can be automatically detected.
- Connect technical monitoring solutions to model governance and operational resilience.