Week 6 Review: Data Quality and Monitoring Foundations
Wire Validation, Drift Checks, and a Monitoring Plan Into One Pipeline That Refuses to Train on Bad Data.
By Sandip Das · · 4 min read
💡
Hey — It's Sandip Das 👋
In Day 41 we wrote a monitoring plan for iris-mlops: four pillars, thresholds and remediation actions. Right now, though, our data checks are separate scripts that someone has to remember to run.
Today we consolidate Week 6: recap what each tool does, then make data validation a real stage in the DVC pipeline so a corrupted dataset stops training automatically.
In Day 41 we wrote a monitoring plan for iris-mlops: four pillars, thresholds and remediation actions. Right now, though, our data checks are separate scripts that someone has to remember to run.
Today we consolidate Week 6: recap what each tool does, then make data validation a real stage in the DVC pipeline so a corrupted dataset stops training automatically.
📚 Key Learnings
- How feature stores, data validation, drift detection and monitoring fit together
- Where each check belongs: before training, at serving, or on a schedule
- Turning a Great Expectations checkpoint into a DVC pipeline stage
- Making the pipeline fail fast on bad data and proving it with a corrupted dataset
- Self-check questions to confirm the week actually stuck
🧠 Learn here
Week 6 in one table
This
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Data Quality