ML Model Performance Monitoring with WhyLabs скачать в хорошем качестве

ML Model Performance Monitoring with WhyLabs

In this session, we'll explore monitoring key metrics and how to monitor different parts of the ML lifecycle to keep your model performing at its best. We'll discuss how WhyLabs can help you streamline your performance monitoring practices and detect issues before they impact your users. Learn how to easily leverage WhyLabs to visualize your data, automate your monitoring workflows, and troubleshoot performance issues. Interested in a 1:1 demo with a solutions architect instead? Sign up here: https://whylabs.ai/contact-us Agenda: The importance of performance monitoring for machine learning models in production Key metrics to monitor, such as accuracy, false positive rate, and throughput. Also, how to include functional metrics from traditional observability tools. How to monitor different parts of the ML lifecycle, including data collection, pre-processing, training, and inference -How WhyLabs can help streamline performance monitoring practices and detect issues before they impact users Leveraging WhyLabs to visualize data, automate monitoring workflows, and troubleshoot performance issues fast Best practices for effective monitoring to keep your model performing optimally in production By the end of this session, you'll have a better understanding of how to effectively monitor your models in production and keep them performing optimally.