Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks.

He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals.

Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify?

// Bio

Sherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs.

Alice is an advanced agentic AI working in production and at scale. Under Sherwood’s leadership, the system grew from initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR.

Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex’s application and engineering org through hypergrowth. He currently lives in San Francisco, CA.

// Related Links

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Connect with Sherwood on LinkedIn: /sherwoodcallaway/

#aiengineering

Timestamps:

[00:00] AI Takes Over Health Calls

[05:05] What Can Agents Really Do?

[08:25] Who’s in Charge—User or Agent?

[11:20] Why Graphs Matter in Agents

[15:03] How Complex Should Agents Be?

[18:33] The Hidden Cost of Model Upgrades

[21:57] Inside the LLM Agent Loop

[25:08] Turning Agents into APIs

[29:06] Scaling Agents Without Meltdowns

[30:04] The Monorepo Tangle, Explained

[34:01] Building Agents the Open Source Way

[38:49] What Production-Ready Agents Look Like

[41:23] AI That Fixes Code on Its Own

[43:26] Tracking Agent Behavior with OpenTelemetry

[46:43] Running Agents Locally with Phoenix

[52:55] LangGraph Meets Arise for Agent Control

[53:29] Hunting Hallucinations in Agent Traces

[56:45] Off-Script Insights Worth Hearing

From the Legal Trenches to Tech // MLOps Podcast #332 with Nick Coleman, Attorney/Founder of LexMed.

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

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// Abstract

Nick Coleman shares his journey from high-volume Social Security disability practice to founding LexMed, a legal tech startup leveraging AI to transform how attorneys handle complex cases. He'll discuss LexMed's dual AI platforms: Hearing Echo, which automates transcription and analysis of disability hearings with speaker identification and critical testimony validation, and ChartVision, which combines human medical abstraction with AI to extract and map medical evidence to disability criteria. Nick will explain how "vibe coding" has dramatically reduced friction between his subject matter expertise and technical implementation, enabling rapid prototyping that preserves legal insights through development. By bridging domain knowledge and technology, LexMed has created solutions that address the real-world challenges he experienced firsthand in his high-volume disability practice, offering valuable lessons for AI implementation in other specialized fields.

// Bio

Nick Coleman is the founder and CEO of LexMed, a legal tech startup applying advanced AI to transform the practice of law. As a Social Security disability attorney with extensive appellate experience, Nick identified critical inefficiencies in legal workflows that technology could solve. LexMed's flagship product, Hearing Echo, leverages speech recognition and natural language processing to automate the transcription and analysis of disability hearing audio, dramatically improving case management for attorneys. Nick holds an AV Preeminent rating from Martindale-Hubbell, has been recognized as a Super Lawyers Rising Star, and serves on the Arkansas Bar Artificial Intelligence Task Force. With deep expertise at the intersection of law and technology, Nick is passionate about democratizing access to justice through innovative AI solutions.

// Related Links

Website: www.lexmed.ai

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Timestamps:

[00:00] Disability Claims Advocacy

[00:29] AI Native Startup

[02:08] Disability Claims Process

[07:56] Tech Journey

[10:52] AI in Document Review

[13:57] Building a Case for Appeal

[19:26] Medical Claims Language Model

[23:37] Tech-Driven Compliance Solutions

[30:31] Claim Prioritization Strategy

[34:57] Wrap up

The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // MLOps Podcast #331 with Frank Meehan, Founder and CEO of Frontier One AI.

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

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

// Abstract

“The awakening of every single country is that they have to control their AI intelligence and not outsource their data" - Jensen Huang. Sovereign AI is rapidly becoming a fundamental national utility, much like defense, energy or telecoms.

Nations worldwide recognize that AI sovereignty—having control over your AI infrastructure, data, and models—is essential for economic progress, security, and especially independence - especially when the US is pushing protectionism and trying to prevent global AI innovation. Of course this has the opposite effect - DeepSeek created by a Hedge Fund in China; India building the world's largest AI data centre (3 GW), and global software teams scaling, learning and building faster than ever before.

However most countries lack the talent, financing and experience to implement Sovereign AI for their requirements - and it is our belief at Frontier One, that one of the biggest markets for AI applications, cloud services and GPUs will be global governments. We see it already - with $10B of GPUs in 2024 bought directly by governments - and it's rapidly expanding. We will talk about what Sovereign AI is - both infrastructure and software details / why it is crucial for a nation / how to get involved as part of the MLOps community.

// Bio

Co-Founder of Frontier One - building Sovereign AI Factories and Cloud software for global markets.

Frank is a 2X CEO | 2X CMO (with 2X exits + 1 IPO NYSE), Board Director (Spotify, Siri) and Investor (SparkLabs Group) with 20+ years of experience in creating and growing leading brands, products and companies.

Chair of Improvability, automating due diligence and reporting for corporates, foundations and Governments with AI.

Co-founder and partner at SparkLabs Group - investors in OpenAI, Anthropic, 88 Rising, Discord, Animoca, Andela, Vectara, Kneron, Messari, Lifesum + 400 companies in our portfolio. Investment Committee and LP at SparkLabs Cultiv8 with 56 investments in consumer food and regenerative agriculture companies.

Co-founder and CMO - later CEO - of Equilibrium AI (Singapore), building it to one of the leading ESG and Carbon data management platforms globally. Equilibrium was acquired by FiscalNote in 2021, where he joined the senior leadership team, running the ESG business globally, and helping the company IPO in 2022 on the NYSE at $1.1B valuation.

Board director at Spotify (2009-2012); Siri (2009-2010 exited to Apple); Lifesum (leading AI health app with 50 million users), seed investor in 88 Rising (Asia’s leading independent music label); CEO/CMO and co-founder at INQ Mobile (mobile internet pioneer); and Global Director for devices and products at 3 Mobile.

Started as a software developer with Ericsson Mobile in Sweden, after graduating from KTH in Stockholm and the University of Sydney with a Bachelor of Mechanical Engineering, and Master of Science in Fluid Mechanics.

// Related Links

https://www.frontierone.ai/ and

https://www.sparklabsgroup.com

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Thanks to MLflow for supporting this episode — the platform helping teams track, manage, and deploy ML and GenAI projects with ease. Try it free at mlflow.org.

What if AI could build and maintain your software—like a co-worker who never forgets state? In this episode, Jiquan Ngiam chats with Demetrios about agents that actually do the work: parsing emails, updating spreadsheets, and reshaping how we design software itself. Less hype, more hands-on AI—tune in for a glimpse at the future of truly personalized computing.

// Bio

Jiquan Ngiam is the Co-Founder and CEO of Lutra AI, with deep expertise in artificial intelligence and machine learning. He was previously at Google Brain, Coursera, and in the Stanford CS Ph.D. program advised by Andrew Ng. He helped develop the first online courses in Machine Learning, and is now building agentic AI systems that can complete tasks for us.

// Related Links

https://www.youtube.com/@LutraAI

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Timestamps:

[00:00] Agents That Actually Do Work

[08:21] Building Tables With AI Help

[12:54] Guardrails for Smarter Code

[16:35 - 18:00] MLFlow Ad[18:30] What’s Next for MCP?

[23:23] AI as Your Data Conductor

[31:13] Rethinking AI + Data Stacks

[32:10] Sandbox Security, Real Risks

[40:48] Smarter Reviews, Powered by Use

[46:08] Cost vs. Quality in AI

[52:00] Podcast Editing Gets Creative

[56:27] Transparent UIs, Powered by AI

[01:00:28] Can AI Learn Good Taste?

[01:04:45] Peeking Into Wild AI Futures

Kai Wang joins the MLOps Community podcast LIVE to share how Uber built and scaled its ML platform, Michelangelo. From mission-critical models to tools for both beginners and experts, he walks us through Uber’s AI playbook—and teases plans to open-source parts of it.

// Bio

Kai Wang is the product lead of the AI platform team at Uber, overseeing Uber's internal end-to-end ML platform called Michelangelo that powers 100% Uber's business-critical ML use cases.

// Related Links

Uber GenAI: https://www.uber.com/blog/from-predictive-to-generative-ai/

#uber #podcast #ai #machinelearning

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Timestamps:

[00:00] Rethinking AI Beyond ChatGPT

[04:01] How Devs Pick Their Tools

[08:25] Measuring Dev Speed Smartly

[10:14] Predictive Models at Uber

[13:11] When ML Strategy Shifts

[15:56] Smarter Uber Eats with AI

[19:29] Summarizing Feedback with ML

[23:27] GenAI That Users Notice

[27:19] Inference at Scale: Michelangelo

[32:26] Building Uber’s AI Studio

[33:50] Faster AI Agents, Less Pain

[39:21] Evaluating Models at Uber

[42:22] Why Uber Open-Sourced Machanjo

[44:32] What Fuels Uber’s AI Team

The Missing Data Stack for Physical AI // MLOps Podcast #328 with Nikolaus West, CEO of Rerun.

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

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

// Abstract

Nikolaus West, CEO of Rerun, breaks down the challenges and opportunities of physical AI—AI that interacts with the real world. He explains why traditional software falls short in dynamic environments and how visualization, adaptability, and better tooling are key to making robotics and spatial computing more practical.

// Bio

Niko is a second-time founder and software engineer with a computer vision background from Stanford. He’s a fanatic about bringing great computer vision and robotics products to the physical world.

// Related Links

Website: rerun.io

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Timestamps:

[00:00] Niko's preferred coffee

[00:35] Physical AI vs Robotics Debate

[04:40] IoT Hype vs Reality

[12:16] Physical AI Lifecycle Overview

[20:05] AI Constraints in Robotics

[23:42] Data Challenges in Robotics

[33:37] Open Sourcing AI Tools

[39:36] Rerun Platform Integration

[40:57] Data Integration for Insights

[45:02] Data Pipelines and Quality

[49:19] Robotics Design Trade-offs

[52:25] Wrap up

LLMs are reshaping the future of data and AI—and ignoring them might just be career malpractice. Yoni Michael and Kostas Pardalis unpack what’s breaking, what’s emerging, and why inference is becoming the new heartbeat of the data pipeline.

// Bio

Kostas Pardalis

Kostas is an engineer-turned-entrepreneur with a passion for building products and companies in the data space. He’s currently the co-founder of Typedef. Before that, he worked closely with the creators of Trino at Starburst Data on some exciting projects. Earlier in his career, he was part of the leadership team at Rudderstack, helping the company grow from zero to a successful Series B in under two years. He also founded Blendo in 2014, one of the first cloud-based ELT solutions.

Yoni Michael

Yoni is the Co-Founder of typedef, a serverless data platform purpose-built to help teams process unstructured text and run LLM inference pipelines at scale. With a deep background in data infrastructure, Yoni has spent over a decade building systems at the intersection of data and AI — including leading infrastructure at Tecton and engineering teams at Salesforce.

Yoni is passionate about rethinking how teams extract insight from massive troves of text, transcripts, and documents — and believes the future of analytics depends on bridging traditional data pipelines with modern AI workflows. At Typedef, he’s working to make that future accessible to every team, without the complexity of managing infrastructure.

// Related Links

Website: https://www.typedef.ai

https://techontherocks.show

https://www.cpard.xyz

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Timestamps:

[00:00] Breaking Tools, Evolving Data Workloads

[06:35] Building Truly Great Data Teams

[10:49] Making Data Platforms Actually Useful

[18:54] Scaling AI with Native Integration

[24:04] Empowering Employees to Build Agents

[28:17] Rise of the AI Sherpa

[36:09] Real AI Infrastructure Pain Points

[38:05] Fixing Gaps Between Data, AI

[46:04] Smarter Decisions Through Better Data

[50:18] LLMs as Human-Machine Interfaces

[53:40] Why Summarization Still Falls Short

[01:01:15] Smarter Chunking, Fixing Text Issues

[01:09:08] Evaluating AI with Canary Pipelines

[01:11:46] Finding Use Cases That Matter

[01:17:38] Cutting Costs, Keeping AI Quality

[01:25:15] Aligning MLOps to Business Outcomes

[01:29:44] Communities Thrive on Cross-Pollination

[01:34:56] Evaluation Tools Quietly Consolidating

What makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical.

// Bio

Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications.By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers.Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools.He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company.

// Related Links

Website: https://gregkamradt.com/

YouTube channel: https://www.youtube.com/@DataIndependent

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Timestamps:

[00:00] Human-Easy, AI-Hard

[05:25] When the Model Shocks Everyone

[06:39] “Let’s Circle Back on That Benchmark…”

[09:50] Want Better AI? Pay the Compute Bill

[14:10] Can We Define Intelligence by How Fast You Learn?

[16:42] Still Waiting on That Algorithmic Breakthrough

[20:00] LangChain Was Just the Beginning

[24:23] Start With Humans, End With AGI

[29:01] What If Reality’s Just... What It Seems?

[32:21] AI Needs Fewer Vibes, More Predictions

[36:02] Defining Intelligence (No Pressure)

[36:41] AI Building AI? Yep, We're Going There

[40:13] Open Source vs. Prize Money Drama

[43:05] Architecting the ARC Challenge

[46:38] Agent 57 and the Atari Gauntlet

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.

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

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

// Abstract

I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.

// Bio

Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.

As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.

// Related Links

Website: https://www.snowleopard.ai/

AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ

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Timestamps:

[00:00] Deepti's preferred coffee

[00:49] MLflow vs Kubeflow Debate

[04:58] GenAI Data Integration Challenges

[09:02] GenAI Sidecar Spicy Takes

[14:07] Troubleshooting LLM Hallucinations

[19:03] AI Overengineering and Hype

[25:06] Self-Serve Analytics Governance

[33:29] Dashboards vs Data Quality

[37:06] Agent Database Context Control

[43:00] LLM as Orchestrator

[47:34] Tool Call Ownership Clarification

[51:45] MCP Server Challenges

[56:52] Wrap up

The Creator of FastAPI’s Next Chapter // MLOps Podcast #322 with Sebastián Ramírez, Developer at FastAPI Labs.

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

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

// Abstract

The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs.

// Bio

Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open source tools.He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem.

// Related Links

Website: https://tiangolo.com/

FastAPI: https://fastapi.tiangolo.com/

FastAPI Cloud: https://fastapicloud.com/

FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg

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Timestamps:

[00:00] Sebastián's preferred coffee

[00:15] Takeaways

[01:43] Why Pydantic is Awesome

[06:47] ML Background and FastAPI

[10:44] NASA FastAPI Emojis

[15:21] FastAPI Cloud Journey

[26:07] FastAPI Cloud Open-Source Balance

[31:45] Basecamp Design Philosophy

[35:30] AI Abstraction Strategies

[42:56] Engineering vs Developer Experience

[51:40] Dogfooding and Docs Strategy

[59:44] Code Simplicity and Trust

[1:04:26] Scaling Without Losing Vision

[1:08:20] FastAPI Cloud Signup

[1:09:23] Wrap up

Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance.

The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success.

Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing.

Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve.

// Bio

Shreya Shankar

PhD student in data management for machine learning.

Willem Pienaar

Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories.

Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high scale ML systems for the Southeast Asian decacorn.

// Related Links

https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_pagehttps://cleric.ai/

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Connect with Shreya on LinkedIn: /shrshnk

Connect with Willem on LinkedIn: /willempienaar

Timestamps:

[00:00] Trust Issues in AI Data

[04:49] Cloud Clarity Meets Retrieval

[09:37] Why Fast AI Is Hard

[11:10] Fixing AI Communication Gaps

[14:53] Smarter Feedback for Prompts

[19:23] Creativity Through Data Exploration

[23:46] Helping Engineers Solve Faster

[26:03] The Three Gaps in AI

[28:08] Alerts Without the Noise

[33:22] Custom vs General AI

[34:14] Sharpening Agent Skills

[40:01] Catching Repeat Failures

[43:38] Rise of Self-Healing Software

[44:12] The Chaos of Monitoring AI

Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks. Join the Community: https://go.mlops.community/YTJoinIn

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

Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.

// Bio

Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.

Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.

// Related Links

Website: https://www.databricks.com/

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Timestamps:

[00:00] Raj's preferred coffee

[00:36] Takeaways

[01:02] Tao Naming Decision

[04:19] No Labels Machine Learning

[08:09] Tao and TAO breakdown

[13:20] Reward Model Fine-Tuning

[18:15] Training vs Inference Compute

[22:32] Retraining and Model Drift

[29:06] Prompt Tuning vs Fine-Tuning

[34:32] Small Model Optimization Strategies

[37:10] Small Model Potential

[43:08] Fine-tuning Model Differences

[46:02] Mistral Model Freedom

[53:46] Wrap up

Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI.

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// Abstract

AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations.

While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.// BioFounder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development.

Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI.

// Related Links

Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform

Jukka's new company:https://8wave.ai

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Timestamps:

[00:00] Jukka's preferred coffee

[00:39] Open-Source Platform Benefits

[01:56] Silo MLOps Platform Explanation

[05:18] AI Model Production Processes

[10:42] AI Platform Use Cases

[16:54] Reproducibility in Research Models

[26:51] Pipeline setup automation

[33:26] MLOps Adoption Journey

[38:31] EU AI Act and Open Source

[41:38] MLOps and 8wave AI

[45:46] Optimizing Cross-Stakeholder Collaboration

[52:15] Open Source ML Platform

[55:06] Wrap up

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

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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?

Raza Habib, the CEO of 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

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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 // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML.

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// 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/

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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

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!

// BioMohan 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 LinksWebsites: https://rafay.co/

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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

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, LlamaIndex 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

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Connect with Demetrios on LinkedIn: /dpbrinkm

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Connect with Sam on LinkedIn: /samparteeTimestamps:[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 // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom .

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// AbstractThe 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 LinksWebsite: https://dealroom.nethttps://www.mascience.com

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

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// 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

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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