How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350
Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being the first to publicly warn about the coronavirus that started in Wuhan. How did the company’s system of algorithms and data processing techniques help flag the potential dangers of the disease? In our conversation, Kamran talks us through how the technology works, its limits, and the motivation behind the wor
19 Feb 202051min
Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and explainability in the context of models, the various types of models covered in the book, and the importance of post-deployment monitoring.
17 Feb 202042min
Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348
Today we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics, which was the recipient of the Best Paper award at the 2019 Black in AI Workshop at NeurIPS. In our conversation, break down the paper and the thought process around AI ethics, the “harm of categorization,” how ML generally doesn’t account for the ethics of various scenarios and how relational ethics could solve the issue, and much more.
13 Feb 202041min
AI for Agriculture and Global Food Security with Nemo Semret - #347
Today we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling.
10 Feb 20201h 4min
Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
Today we’re joined by Ryan Rogers, Senior Software Engineer at LinkedIn, to discuss his paper “Practical Differentially Private Top-k Selection with Pay-what-you-get Composition.” In our conversation, we discuss how LinkedIn allows its data scientists to access aggregate user data for exploratory analytics while maintaining its users’ privacy through differential privacy, and the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise.
7 Feb 202033min
Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345
Today we conclude the KubeCon ‘19 series joined by Erez Cohen, VP of CloudX & AI at Mellanox, who we caught up with before his talk “Networking Optimizations for Multi-Node Deep Learning on Kubernetes.” In our conversation, we discuss NVIDIA’s recent acquisition of Mellanox, the evolution of technologies like RDMA and GPU Direct, how Mellanox is enabling Kubernetes and other platforms to take advantage of the recent advancements in networking tech, and why we should care about networking in Deep Lea
5 Feb 202031min
Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
Today we’re joined by Bob Killen, Research Cloud Administrator at the University of Michigan. In our conversation, we explore how Bob and his group at UM are deploying Kubernetes, the user experience, and how those users are taking advantage of distributed computing. We also discuss if ML/AI focused Kubernetes users should fear that the larger non-ML/AI user base will negatively impact their feature needs, where gaps currently exist in trying to support these ML/AI users’ workloads, and more!
3 Feb 202025min
Scalable and Maintainable Workflows at Lyft with Flyte w/ Haytham AbuelFutuh and Ketan Umare - #343
Today we kick off our KubeCon ‘19 series joined by Haytham AbuelFutuh and Ketan Umare, a pair of software engineers at Lyft. We caught up with Haytham and Ketan at KubeCo, where they were presenting their newly open-sourced, cloud-native ML and data processing platform, Flyte. We discuss what prompted Ketan to undertake this project and his experience building Flyte, the core value proposition, what type systems mean for the user experience, how it relates to Kubeflow and how Flyte is used across Lyft.
30 Jan 202045min
