ML Monitoring: Data Drift, Quality, Bias and Explainability

May 24, 03:00 PM PDT
  • Virtual WhyLabs
  • 201 RSVP

If you want to build reliable pipelines, trustworthy data, and responsible AI applications, you need to validate and monitor your data & ML models!

In this workshop we’ll cover how to ensure model reliability and performance to implement your own AI observability solution from start to finish. We will cover:
- Detecting data drift
- Measuring model drift
- Monitoring model performance
- Data quality validation
- Measuring Bias & Fairness
- Model explainability

What you’ll need:
- A modern web browser
- A Google account (for saving a Google Colab)
- Sign up free a free WhyLabs account (

Who should attend:
Anyone interested in AI Observability, Model monitoring, MLOps, and DataOps! This workshop is designed to be approachable for most skill levels. Familiarity with machine learning and Python will be useful, but not required.

By the end of this workshop, you’ll be able to implement data and AI observability into your own pipelines (Kafka, Airflow, Flyte, etc) and ML applications to catch deviations and biases in data or ML model behavior.

You will receive a certificate for completing workshop.

Sage Elliott

Sage Elliott enjoys breaking down the barrier to AI observability, talking to amazing people in the Robust & Responsible AI community, and teaching workshops on machine learning. Sage has worked in hardware and software engineering roles at various startups for over a decade. Connect with Sage on LinkedIn:
The event ended.
Watch Recording
*Recordings hosted on Youtube, click the link will open the Youtube page.
Contact Organizer