ML Drift - How to Identify Issues Before They Become Problems

Name: ML%20Drift%20-%20How%20to%20Identify%20Issues%20Before%20They%20Become%20Problems
Uploaded: 2021-12-08T09:00:00.000Z

Posted Dec 08, 2021 | Views 553

# Monitoring

# Presentation

# ML Drift

# ML

# Fiddler

# Fiddler.ai

Amy Hodler

Executive Director @ GraphGeeks

Amy Hodler is an evangelist for graph analytics and responsible AI. She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics and Bloomsbury book, AI on Trial. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, Cray, and RelationalAI. Amy is the founder of GraphGeeks.org promoting connections everywhere.

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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Over time, our AI predictions degrade. Full Stop. Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy. Attend this meetup to understand the key types of machine learning drift and how to catch them before they become problems.

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