The Data Phoenix Events team invites you all today (August 25) to the second of our series of "The A-Z of Data" webinars. The topic — Monitoring ML Models in Production.
The performance of machine learning models can decline over time - due to changes in data, business processes, or simply data loss or failures. To avoid the negative impact on business performance, it is crucial to detect such situations and take timely action - for example, by retraining the model. Due to this, monitoring of services based on machine learning needs to include additional metrics related to the model and data quality. In the course of the tutorial, Emeli Dral will demonstrate how the quality of a model can change over time, and how one can track and analyze the changes using open source tools.
Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing open-source tools to analyze and monitor the performance of machine learning models. Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at GSOM SpBU and Harbour. Space University. She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students. She also co-founded Data Mining in Action, the largest open data science course in Russia.
Participation is free, but pre-registration is required.
"The A-Z of Data" — A series of webinars from Data Phoenix Events designed to help data scientists, data engineers, machine learning engineers and all interested in data to expand the horizons of their data expertise. The webinars will be divided into subject blocks and every block will consist of an overview webinar, several technical events about best tools / practices / approaches / model architectures, as well as a webinar with practical use cases and a discussion panel with experts. In 2021, we plan to cover: MLOps, NLP, CV, and Time-Series Forecasting.
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