Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML.
In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, and his thoughts on why experiment management has become an automatic buy, be it ... Show More
Apr 16
How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765
In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a highly regulated environment. Rashmi walks us through Chat Concierge, a multi-age ... Show More
54m 18s
Mar 26
The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764
Today, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used for images—are being adapted for text and code generation, the technical challe ... Show More
1h 3m
Mar 10
Agent Swarms and Knowledge Graphs for Autonomous Software Development with Siddhant Pardeshi - #763
In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” an ... Show More
1h 16m
May 2024
2882: From Chess Grandmaster to ML Innovator: Tal Shaked's Journey
<p>Are machines really capable of thinking like humans, or are we merely programming them to mimic our own patterns? Today on Tech Talks Daily, we delve into this intriguing question with Tal Shaked, an American chess grandmaster and Chief Machine Learning Officer at Moloco, a le ... Show More
29m 26s
Mar 2022
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
<p>This is one episode where passion for math, statistics and computers are merged.
I have a very interesting conversation with Ravin, data scientist at Google where he uses data to inform decisions.</p>
<p>He has previously worked at Sweetgreen, designing systems that would b ... Show More
31m 12s
Feb 2024
#179 Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author
We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.In a similar ... Show More
48 m
Sep 2021
Declarative Machine Learning Without The Operational Overhead Using Continual
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies th ... Show More
1h 11m
