Agentic Conversations (formally mlops.community)

Latest Episodes:

Hard Learned Lessons from Over a Decade in AI Jun 06, 2025

Show notes

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.

// Bio

Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google, where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.

// Related Links

Website: ⁠www.tecton.ai⁠

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠

MLOps Swag/Merch: [⁠https://shop.mlops.community/⁠]

Connect with Demetrios on LinkedIn: ⁠/dpbrinkm⁠

Connect with Mike on LinkedIn: ⁠/michaeldelbalso⁠

Timestamps:

[00:00] Smarter decisions, less manual work

[03:52] Data pipelines: pain and fixes

[08:45] Why Tecton was born

[11:30] ML use cases shift

[14:14] Models for big bets

[18:39] Build or buy drama

[20:20] Fintech's data playbook

[23:52] What really needs real-time

[28:07] Speeding up ML delivery

[32:09] Valuing ML is tricky

[35:29] Simplifying ML toolkits

[37:18] AI copilots in action

[42:13] AI that fights fraud

[45:07] Teaming up across coasts

[46:43] Tecton + Generative AI?

Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320 Jun 03, 2025

Show notes

Raza Habib, the CEO of the LLM Eval platform Humanloop, talks to us about how to make your AI products more accurate and reliable by shortening the feedback loop of your evals. Quickly iterating on prompts and testing what works, along with some of his favorite Dario from Anthropic AI Quotes.

// Bio

Raza is the CEO and Co-founder at Humanloop. He has a PhD in Machine Learning from UCL, was the founding engineer of Monolith AI, and has built speech systems at Google. For the last 4 years, he has led Humanloop and supported leading technology companies such as Duolingo, Vanta, and Gusto to build products with large language models. Raza was featured in the Forbes 30 Under 30 technology list in 2022, and Sifted recently named him one of the most influential Gen AI founders in Europe.

// Related Links

Websites: https://humanloop.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Raza on LinkedIn: /humanloop-raza

Timestamps:

[00:00] Cracking Open System Failures and How We Fix Them

[05:44] LLMs in the Wild — First Steps and Growing Pains

[08:28] Building the Backbone of Tracing and Observability

[13:02] Tuning the Dials for Peak Model Performance

[13:51] From Growing Pains to Glowing Gains in AI Systems

[17:26] Where Prompts Meet Psychology and Code

[22:40] Why Data Experts Deserve a Seat at the Table

[24:59] Humanloop and the Art of Configuration Taming

[28:23] What Actually Matters in Customer-Facing AI

[33:43] Starting Fresh with Private Models That Deliver

[34:58] How LLM Agents Are Changing the Way We Talk

[39:23] The Secret Lives of Prompts Inside Frameworks

[42:58] Streaming Showdowns — Creativity vs. Convenience

[46:26] Meet Our Auto-Tuning AI Prototype

[49:25] Building the Blueprint for Smarter AI

[51:24] Feedback Isn’t Optional — It’s Everything

Getting AI Apps Past the Demo // Vaibhav Gupta // #319 May 30, 2025

Show notes

Getting AI Apps Past the Demo // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here?

// Bio

Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision!

// Related Links

Website: https://www.boundaryml.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Vaibhav on LinkedIn: /vaigup

Timestamps:

[00:00] Vaibhav's preferred coffee

[00:38] What is BAML

[03:07] LangChain Overengineering Issues

[06:46] Verifiable English Explained

[11:45] Python AI Integration Challenges

[15:16] Strings as First-Class Code

[21:45] Platform Gap in Development

[30:06] Workflow Efficiency Tools

[33:10] Surprising BAML Insights

[40:43] BAML Cool Projects

[45:54] BAML Developer Conversations

[48:39] Wrap up

Building Out GPU Clouds // Mohan Atreya // #317 May 23, 2025

Show notes

Demetrios and Mohan Atreya break down the GPU madness behind AI — from supply headaches and sky-high prices to the rise of nimble GPU clouds trying to outsmart the giants. They cover power-hungry hardware, failed experiments, and how new cloud models are shaking things up with smarter provisioning, tokenized access, and a whole lotta hustle. It's a wild ride through the guts of AI infrastructure — fun, fast, and full of sparks!

Big thanks to the folks at Rafay for backing this episode — appreciate the support in making these conversations happen!

// Bio

Mohan is a seasoned and innovative product leader currently serving as the Chief Product Officer at Rafay Systems. He has led multi-site teams and driven product strategy at companies like Okta, Neustar, and McAfee.

// Related Links

Websites: https://rafay.co/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Mohan on LinkedIn: /mohanatreya

Timestamps:

[00:00] AI/ML Customer Challenges

[04:21] Dependency on Microsoft for Revenue

[09:08] Challenges of Hypothesis in AI/ML

[12:17] Neo Cloud Onboarding Challenges

[15:02] Elastic GPU Cloud Automation

[19:11] Dynamic GPU Inventory Management

[20:25] Terraform Lacks Inventory Awareness

[26:42] Onboarding and End-User Experience Strategies

[29:30] Optimizing Storage for Data Efficiency

[33:38] Pizza Analogy: User Preferences

[35:18] Token-Based GPU Cloud Monetization

[39:01] Empowering Citizen Scientists with AI

[42:31] Innovative CFO Chatbot Solutions

[47:09] Cloud Services Need Spectrum

A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live May 21, 2025

Show notes

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful.

// Bio

Sam Partee

Sam Partee is the CTO and Co-Founder of Arcade AI. Previously, a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects, including Langchain, DeterminedAI, LlamaInde,x, and Chapel, amongst others. While at Cray/HPE, he created the SmartSim AI framework, which is now used at national labs around the country to integrate HPC simulations like climate models with AI.

Rahul Parundekar

Rahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC, working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations) and AI system scalability. Other than his full-time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community.

// Related Links

Websites: arcade.dev // aihero.studio

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Rahul on LinkedIn: /rparundekar

Connect with Sam on LinkedIn: /sampartee

Timestamps:

[00:00] Agents & Tools, Explained (Without Melting Your Brain)

[09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It)

[13:18] Can We Actually Trust the Protocol?

[18:13] KYC, But Make It AI (and Less Painful)

[25:25] Web Automation Tests: The Bugs Strike Back

[28:18] MCP Dev: What Went Wrong (and What Saved Us)

[33:53] Social Login: One Button to Rule Them All

[39:33] What Even Is an AI-Native Developer?

[42:21] Betting Big on Smarter Models (High Risk, High Reward)

[51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks)

[55:31] Async Task Handoffs: Herding Cats, But Digitally

[1:00:37] Getting AI to Actually Help Your Workflow

[1:03:53] The Infamous Varma System Error (And How We Dodge It)

AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 May 16, 2025

Show notes

AI in M&A: Building, Buying, and the Future of Dealmaking // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space.

// Bio

Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads.

Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices.

Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value.

// Related Links

Website: https://dealroom.net

https://www.mascience.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Kison on LinkedIn: /kisonpatel

Timestamps:

[00:00] Kison's preferred coffee

[00:11] Takeaways

[00:40] Founders' Journey Slumps

[05:07] Jira for M&A

[10:57] Overcoming Idea Paralysis

[14:32] Customer-led Discovery Success

[22:20] Legal Fees in Deals

[26:24] Data Room Differentiators

[29:26] PLG vs Sales Teams

[31:43] AI Pricing Strategies

[35:15] PLG AI Cost Optimization

[40:53] Building AI Teams

[47:40] Great Companies Are Bought

[51:10] M&A Failures and Fever

[54:23] Wrap up

AI, Marketing, and Human Decision Making // Fausto Albers // #313 May 14, 2025

Show notes

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.

// Bio

Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck.

His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.

Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.

// Related Links

Website: aibuilders.club

Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Fausto on LinkedIn: /stepintoliquid

Timestamps:

[00:00] Fausto's preferred coffee

[00:26] Takeaways

[01:18] Automated Ad Creative Generation

[07:14] AI in Marketing Workflows

[13:23] MCP and System Bottlenecks

[21:45] Forward Compatibility vs Optimization

[29:57] Unlocking Workflow Speed

[33:48] AI Dependency vs Critical Thinking

[37:44] AI Realism and Paradoxes

[42:30] Outsourcing Decision-Making Risks

[46:22] Human Value in Automation

[49:02] Wrap up

MLOps with Databricks // Maria Vechtomova // #314 May 13, 2025

Show notes

MLOps with Databricks // MLOps Podcast #314 with Maria Vechtomova, MLOps Tech Lead | Founder at Ahold Delhaize | Marvelous MLOps.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case and walk you through Databricks MLOps features.

// Bio

Maria is an MLOps Tech lead with over 10 years of experience in Data and AI.

For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations.

Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs.

// Related Links

Website: marvelousmlops.io

MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Maria on LinkedIn: /maria-vechtomova

Timestamps:

[00:00] Maria's preferred coffee

[00:42] Takeaways

[02:48] Why Databricks for MLOps

[09:56] Platform Adoption vs Procurement Pain

[12:56] Databricks Best Practices

[16:57] Feature Store Overview

[22:00] Managed system trade-offs

[29:15] Databricks Developments and Trends

[44:31] Insider Info and Summit

[45:47] Data Ownership Pros and Cons

[48:08] Data Contracts and Challenges

[51:25] MLOps Databricks Book Guide

[52:19] Wrap up

Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312 May 06, 2025

Show notes

Making AI Reliable is the Greatest Challenge of the 2020s // MLOps Podcast #312 with Alon Bochman, CEO of RagMetrics.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

Huge shout-out to RagMetrics for sponsoring this episode!

// Abstract

Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators.

// Bio

Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager. Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player.

// Related Links

Website: ragmetrics.ai

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alon on LinkedIn: /alonbochman

Timestamps:

[00:00] Alon's preferred coffee

[00:15] Takeaways

[00:47] Testing Multi-Agent Systems

[05:55] Tracking ML Experiments

[12:28] AI Eval Redundancy Balance

[17:07] Handcrafted vs LLM Eval Tradeoffs

[28:15] LLM Judging Mechanisms

[36:03] AI and Human Judgment

[38:55] Document Evaluation with LLM

[42:08] Subject Matter Expertise in Co-Pilots

[46:33] LLMs as Judges

[51:40] LLM Evaluation Best Practices

[55:26] LM Judge Evaluation Criteria

[58:15] Visualizing AI Outputs

[1:01:16] Wrap up

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311 May 02, 2025

Show notes

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // MLOps Podcast #311 with Devansh Devansh, Head of AI at Stealth AI Startup.

Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Open-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure.

// Bio

The best meme-maker in Tech. Writer on AI, Software, and the Tech Industry.

// Related Links

Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/

https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Devansh on LinkedIn: /devansh-devansh-516004168