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
Nov 19
Proactive Agents for the Web with Devi Parikh - #756
Today, we're joined by Devi Parikh, co-founder and co-CEO of Yutori, to discuss browser use models and a future where we interact with the web through proactive, autonomous agents. We explore the technical challenges of creating reliable web agents, the advantages of visually-gro ... Show More
56m 4s
Nov 12
AI Orchestration for Smart Cities and the Enterprise with Robin Braun and Luke Norris - #755
Today, we're joined by Robin Braun, VP of AI business development for hybrid cloud at HPE, and Luke Norris, co-founder and CEO of Kamiwaza, to discuss how AI systems can be used to automate complex workflows and unlock value from legacy enterprise data. Robin and Luke detail high ... Show More
54m 46s
Nov 4
Building an AI Mathematician with Carina Hong - #754
In this episode, Carina Hong, founder and CEO of Axiom, joins us to discuss her work building an "AI Mathematician." Carina explains why this is a pivotal moment for AI in mathematics, citing a convergence of three key areas: the advanced reasoning capabilities of modern LLMs, th ... Show More
55m 52s
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