Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336

Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336

Author: Demetrios August 27, 2025 Duration: 57:35

Distilling 200+ Hours of NeurIPS: What’s Next for AI // MLOps Podcast #336 with Nikolaos Vasiloglou, VP of Research ML at RelationalAI.


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

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


// Abstract

Nikolaos' widely shared analysis on LinkedIn highlighted key insights across agentic AI, scaling laws, LLM development, and more. Now, he’s exploring how AI itself might be trained to automate this process in the future, offering a glimpse into how researchers could harness LLMs to synthesize conferences like NeurIPS in real-time.


// Bio

Nikolaos Vasiloglou is VP of Research-ML for RelationalAI, the industry's first knowledge graph coprocessor for the data cloud. Nikolaos has over 20 years of experience implementing high-value machine learning and AI solutions across various industries.


// Related Links

Website: https://relational.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)]

Sign up for the next meetup: [https://go.mlops.community/register]

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


Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Nikolaos on LinkedIn: /vasiloglou/


Timestamps:

[00:00] Nik's preferred coffee

[01:05] Distilling NeurIPS insights

[06:43] Choosing research papers

[16:49] Agent patterns at NeurIPS

[21:16] Interest in agent-based innovation

[25:54] Time series forecasting models

[28:15] Tabular foundation models

[36:25] Verifier challenges and complexity

[39:36] Knowledge graph

[45:00] Knowledge graph data challenges

[47:14] Worldview in knowledge graphs

[50:30] Self-serve analytics challenges

[56:22] Llama model adaptation comparison

[56:59] Wrap up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

MLOps.community
Podcast Episodes
Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality [not-audio_url] [/not-audio_url]

Duration: 42:19
Pramod Krishnan is a Managing Director - AI Managed Services at PwC, specializing in enterprise AI transformation — helping large organizations move from AI experimentation to production operating models. In this episode…
Agents are Just While Loops [not-audio_url] [/not-audio_url]

Duration: 41:11
Hamza Tahir, co-founder of ZenML, joins the show to cut through the hype around long-running agents — arguing that at the end of the day, an agent is just a while loop that talks to a model, calls a tool, and writes to a…
The Latency Goldilocks Zone Explained [not-audio_url] [/not-audio_url]

Duration: 48:13
Rafael (Head of Innovation, iFood) and Daniel (Data and AI Manager, iFood) pull back the curtain on ILO-Agent — iFood's conversational AI ordering system built for 200 million users across Latin America. Recorded live at…
Building MCP Before MCP Existed: Inside Despegar's Sofia Agent [not-audio_url] [/not-audio_url]

Duration: 41:13
Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a workin…
Voice Agent Use Cases [not-audio_url] [/not-audio_url]

Duration: 51:04
This episode is brought to you by the MLflow team. Check out more information at MLflow.org.What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI enginee…
It's 2026, and We're Still Talking Evals [not-audio_url] [/not-audio_url]

Duration: 40:56
Maggie Konstanty is an AI Product Manager at Prosus, one of the world's largest consumer internet companies, where she builds and evaluates AI agents for food ordering and ecommerce at scale. She's been inside the messy…
Why Agents are Driving Software Development to the Cloud [not-audio_url] [/not-audio_url]

Duration: 51:07
This episode is brought to you by Hyperbolic and the MLflow team. Check out more information at hyperbolic.ai and MLflow.org.Why AI Coding Agents Are Moving to the Cloud — With Zach Lloyd, CEO of WarpZach Lloyd is the fo…
The Modern Software Engineer [not-audio_url] [/not-audio_url]

Duration: 53:37
This episode is brought to you by the MLflow team. Check out more information at MLflow.org.Mihail Eric is Head of AI at Monaco and Adjunct Lecturer at Stanford University, where he teaches CS146S: "The Modern Software D…
We Cut LLM Latency by 70% in Production [not-audio_url] [/not-audio_url]

Duration: 1:05:20
Maher Hanafi is an engineering leader who went from zero AI experience to self-hosting LLMs at enterprise scale — managing GPU costs, optimizing inference with TensorRT LLM, and building an AI platform for HR tech. In th…