The Problem With Airflow // Stephen Bailey Stephen has worked as a data scientist, analyst, manager, and engineer, and loves all the domains equally. He currently works at Whatnot, a collectibles marketplace that focuses on live shopping, and has previously worked in privacy tech at Immuta. He has his Ph.D. from Vanderbilt University in educational cognitive neuroscience, but it has yet to help him understand why his three children are so crazy.
AI in the Clouds, Navigating the Hybrid Sky with Ease:
Michael Balint (NVIDIA) and Gijsbert Janssen van Doorn (Run:ai) will discuss how organizations can successfully implement a hybrid cloud strategy for their AI workloads, including various use cases such as when dealing with sensitive data.
Best practices for monitoring, provisioning, and managing GPU environments:
Raz
Rotenberg, Run:ai Software Group Lead will review best practices to provision, monitor, and utilize GPU clusters. We’ll explore open-source tools that can help manage bare metal GPU environments and how a platform like Run:ai delivers GPU Scheduling, GPU Management, and GPU Fractioning to allow ML teams to squeeze 10X out of each GPU and cluster.
**Sponsored Content - Click below to help me pay the bills!**
AI Software Evangelist at Intel. Adrian graduated from the Gdansk University of Technology in the field of Computer Science 6 years ago. After that, he started his career in computer vision and deep learning. As a team leader of data scientists and Android developers for the previous two years, Adrian was responsible for an application to take a professional photo (for an ID card or passport) without leaving home. He is a co-author of the LandCover.ai dataset and he was teaching people how to do deep learning. His current role is to educate people about OpenVINO Toolkit. In his free time, he’s a traveler. You can also talk with him about finance, especially investments.
Blog post
Aurora’s Data Engine: How We Accelerate Machine Learning Model Workflows Aurora's autonomous vehicle development team has created a centralized ML orchestration layer that streamlines the Data Engine lifecycle, the backbone of AV development, by addressing pain points such as lack of automation and cohesion in the model development workflow. This includes improvements such as continuous delivery of datasets and models, automated tests for overall health of end-to-end workflows, and a unified user interface for developers to visualize and debug the entire lifecycle in one place.
Thanks for reading. This issue was written by Demetrios Brinkmann and Jessica Rudd. See you in Slack, Youtube, and podcast land. Oh yeah, and we are also on Twitter if you like chirping birds.
