Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488

Buy AND Build for Production Machine Learning...

Up next

How AI Learns to Smell with Alex Wiltschko - #771

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of olfactory receptors in the human nose to ...  Show more

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of static guardrails plus human approval sta ...  Show more

Recommended Episodes

Serverless NLP Model Training
Data Skeptic

<span class="s1">Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline.<span class= "Apple-converted-space"> </span> The is a technical deep dive on architecting solutions and a discussion of ...

  Show more

LLMs for Data Analysis
Data Skeptic

In this episode, we are joined by Amir Netz, a Technical Fellow at Microsoft and the CTO of Microsoft Fabric. He discusses how companies can use Microsoft's latest tools for business intelligence. Amir started by discussing how business intelligence has progressed in relevance ov ...  Show more

AI, Cloud Computing, and Leadership
IT Visionaries

On today’s episode of IT Visionaries, we are joined by Japjit Tulsi, the CTO of Carta. In his 20 year career, Japjit has led engineering teams at Google, Microsoft, and eBay. He’s helped build products like Google Analytics and, most recently, ShopBot, eBay’s AI tool which com ...

  Show more

MLOps is NOT Real
Practical AI

We all hear a lot about MLOps these days, but where does MLOps end and DevOps begin? Our friend Luis from OctoML joins us in this episode to discuss treating AI/ML models as regular software components (once they are trained and ready for deployment). We get into topics including ...  Show more