Build Drift Detection Pipeline
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 7. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s): - Week 7: AI Observability and LLMOps (slug: ai-observability-107) 🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp. Implement drift detection for AI systems using statistical methods and embedding similarity. Monitor input distributions, detect model behavior changes, and build automated alerting for drift events.
Lab Overview
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 7. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 7: AI Observability and LLMOps (slug: ai-observability-107)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
Implement drift detection for AI systems using statistical methods and embedding similarity. Monitor input distributions, detect model behavior changes, and build automated alerting for drift events.
What You'll Learn
Implement statistical drift detection using Kolmogorov-Smirnov tests
Build embedding-based drift monitoring
Create automated drift detection pipelines
Configure alerting for drift events
Prerequisites
Python programming with numpy
Basic statistics understanding
Understanding of embeddings
Technologies Covered
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