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.
In this episode
Evangelist, Responsible AI, Fiddler
Amy helps organizations see how they can achieve more responsible AI by improving machine learning explainability, accuracy, and bias detection. As the AI evangelist for Fidder Labs, she educates data scientists on the use of continuous monitoring for modern MLOps. Amy is the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the upcoming book, AI on Trial. Amy has consistently helped teams apply novel approaches to generate new opportunities working at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, and Cray. Amy has a love for science history and a fascination for complexity studies.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.