Join Databricks and Microsoft as we share how you can easily scale your single-node PyTorch deep learning models using Azure Databricks and Azure Machine Learning. We’ll show how Azure Databricks enables you to optimize your models by kicking off many training in parallel without having to make significant changes. Once you have tuned your model, we will show how you can easily put it into production as a web service for applications. We will also show how you can detect and correct model drift over time using MLflow and Delta Lake. See how the right combination of services, integrated in the right way, makes all the difference!
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. The key downside to this architecture is the development and operational overhead of managing two different systems. But, with the advent of Delta Lake, we are seeing more enterprises adopt a simple continuous data flow model to process data as they arrive. We call this architecture, The Delta Architecture. In this webinar, we cover the major bottlenecks for adopting a continuous data flow model and how the Delta architecture solves those problems.