Discover how customers are using Fiddler for monitoring, explainability, and model analysis. MLOps Engineers and Data Scientists get hands-on experience on how to instrument ML models with Fiddler for observability. Using a dedicated Fiddler cloud environment on AWS, the register a model and publish model events into the Fiddler Model Performance Management platform. We use the insights surfaced by Fiddler to better understand data drift, data integrity, and underperforming cohorts. This should be a hot topic for customers driving critical ML initiatives and we hope to see you there.
In this episode
Solutions Engineer, Fiddler AI
Danny is a senior solutions engineer at Fiddler. When you boil it down, this role is really about evangelizing the value of Fiddler to our prospects and customers through product demonstrations or evaluations with their own models and data. Danny has to understand the specifics around customer challenges to ensure they get maximum value from Fiddler. Prior to Fiddler, Danny worked for a handful of startups in the analytics space like Endeca, Incorta, and Branchbird which was his own consulting company focused on Hadoop implementations.
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.
Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.