Abstract: 

Flyte is a Kubernetes native, workflow automation platform for business critical Machine learning and Data science workflows. It enables the user to focus on the business logic, while alleviating the management of infrastructure to a centralized team. It also enables platform teams to provide a self serve platform for their users.

This talk should enable you to answer the question if Flyte is right for your organization, what usecases can be best serviced by Flyte.

Session Outline
In this talk we will cover
Part 1: An Overview and use cases for Flyte
In this part we will cover the considerations, trade-offs and motivation that led to the design of Flyte. We will also provide an overview of the open source community, give you a glimpse of the roadmap and overview of the various features of Flyte.

Part 2: Deep dive into an example application written using Flyte.
In this part we will provide a hands on tutorial of writing your first Flyte workflow. Should you the process of taking it from ideation to production. We will also show you, how you use the Flyte API system to introspect and dive deeper into your execution's details.

Part 3: Hands on tutorial on extending Flyte
We realized that every organization may have different needs and existing Flyte may not already fulfill these integrations in some cases. This is why Flyte is build to be extremely extensible. We will showcase some of the extensibility points and also do a hands on tutorial of developing your own extension.

Part 4: Q&A

Background Knowledge
Laptop with docker installed. Knowledge of the python programming language.

Bio: 

Ketan Umare is the TSC Chair for Flyte (incubating under LF AI & Data). He is also currently the Chief Software Architect at Union.ai. Previously he had multiple Senior Lead roles at Lyft, Oracle and Amazon ranging from Cloud, Distributed storage, Mapping (map making) and machine learning systems. He is passionate about building software that makes developer and other engineers' lives easier and provides simplified access to large scale systems. With Flyte he is trying to bridge gap from ideation to productionization for data and ML pipelines and bring a battle tested approach and structure to the data and ML world.