Define, Manage and Validate Models with Ease

MLOps holds the potential to change the way we understand and use the data in an organization. It focuses on scalability and greater collaboration for machine learning models to be production and deployment ready. Along the way, there may be many challenges, especially during the training and inference process.

Let the experts guide you on how to manage, define, discover, validate, and serve features to models at the time of training and inference using KubeFlow Feast. Also, learn about how to detect frauds and rank drivers, and more.

Join us to learn:

MLOps Overview
KubeFlow Overview
MLOps Pipeline
What is Feature Engineering
What is Feast
Fraud Detection example with KubeFlow Feast
Driver Ranking example with KubeFlow Feast
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