04-27, 16:15–17:00 (America/Los_Angeles), Kodiak Theatre
There is a pressing need for tools and workflows that meet data scientists where they are: how to enable an organization of data scientists, who may not have formal training as software engineers, to build and deploy end-to-end machine learning workflows and applications independently.
We wanted to provide the best possible user experience for data scientists, allowing them to focus on the parts of the ML stack where they can deliver the most value (such as modeling using their favorite off-the-shelf libraries) while providing secure & robust built-in solutions for the underlying infrastructure (including data, compute, orchestration, and versioning). In this talk, we discuss the problem space, our enterprise-scale challenges at Dell, and the approach we took to solving it with Metaflow, the open-source ML platform developed at Netflix, & Outerbounds.
In this talk, you will learn about :
- Dell’s enterprise-scale ML workloads for customer engagement solutions
- Expectations from a modern ML infrastructure stack at Dell.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with an enterprise’s systems and policies.
No previous knowledge expected
Savin is the co-founder and CTO of Outerbounds - where his team is building the modern ML stack to accelerate the impact of data science. Previously, he was at Netflix, where he built and open-sourced Metaflow, a full-stack framework for data science.
Thiagu supports Dell’s Infrastructure Customer Service Division and Global Datacenter Sales Data Science team. As a Sr. Machine Learning Engineer at Dell, Thiagu’s responsible for building the foundational infrastructure and evangelizing the best practices necessary to develop and deploy AI/ML models to Dell’s stakeholders. Prior to joining Dell, he was working with Teradata as a Sr. Software Engineer on their database and analytics team.