We’ll take a hands-on dive into implementing the 1st level of MLOps maturity and performing continuous training of the model by automating our ML pipeline. We’ll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.

In this webinar, we’ll learn how to implement the 1st level of MLOps maturity and perform continuous training of the model by automating the ML pipeline. We’ll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.

This session will cover architectural considerations for multi-tenancy in ML, best practices in traditional software engineering that can be copy/pasted over to MLOps, as well as new considerations unique to ML

The Pachyderm and Superwise teams discuss how to build a scalable and automated platform, manage big data, and models retraining

Superwise ML observability platform walkthrough – model integration, production behavior analysis, monitoring policy configs, and retraining strategies

Back by popular demand! Let’s take a dive into MLOps CI/CD + CT pipeline automation. In part 1, we’ll focus on how to put together a continuous ML pipeline to train, deploy, monitor, and retrain your models. Part 2 will focus on automations and production-first insights to detect and resolve issues faster.