Taking a model from research to production is hard — and keeping it there is even harder! As more machine learning models are deployed into production, it’s imperative to have the right skillset to monitor, troubleshoot, and explain model performance. That’s why Arize is hosting an ML Observability Workshop Series to help data scientists and ML practitioners gain confidence taking their models from research to production.
Each week, we will cover a key area of ML Observability and practical applications. You will gain a hands-on understanding of how to identify where a model is underperforming, troubleshoot model and data issues, and how to proactively mitigate future degradations.
Upon completion of this series, you will receive a ML Observability Fundamentals acknowledgement for your new skills!