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The Era of AI Agents in Marketing // Joel Horwitz // #337
The Era of AI Agents in Marketing // MLOps Podcast #337 with Joel Horwitz, Growth Engineer at Neoteric3D.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractWe’re entering a new era in marketing—one powered by AI agents, not just analysts. The rise of tools like Clay, Karrot.ai, 6sense, and Mutiny is reshaping how go-to-market (GTM) teams operate, making room for a new kind of operator: the GTM engineer. This hybrid role blends technical fluency with growth strategy, leveraging APIs, automation, and AI to orchestrate hyper-personalized, scalable campaigns. No longer just marketers, today’s GTM teams are builders—connecting data, deploying agents, and fine-tuning workflows in real time to meet buyers where they are. This shift isn’t just evolution—it’s a replatforming of the entire GTM function.// BioJoel S. Horwitz has been riding the data wave since before it was cool—literally. He spoke at Spark Summit back in 2014 and penned a prescient piece for MIT Tech Review on data science and machine learning before they became boardroom buzzwords. A former big tech executive turned entrepreneur, Joel now runs Neoteric3D (N3D for short), a digital design and data growth agency that helps brands scale with smarts and style. When he’s not architecting next-gen growth strategies, you’ll find him logging long miles on the trail or coaching his sons’ soccer and baseball teams like a champ.// Related LinksWebsite: https://www.neoteric3d.com~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Joel on LinkedIn: /joelshorwitzTimestamps:[00:00] Joel's preferred coffee[00:53] Agentic workflows in marketing[04:26] Agentic AI vs big data[08:24] Creative outreach automation[13:08] LLMs in marketing optimization[17:36] Traffic relevance[23:36] End-to-end AI workflow[28:10] AI in task automation[32:08] AI systems architecting[38:00] AI vs Thought Leadership[43:10] AI as sparring partner[45:22] AI shifts human roles[48:23] Wrap up
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Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336
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/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractNikolaos 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.// BioNikolaos 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 LinksWebsite: https://relational.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect 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
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Building Coding Agents: Design Decisions, Prompting Tricks, GUI Anti-patterns
AI Conversations Powered by Prosus Group Demetrios chats with Beyang Liu about Sourcegraph’s AMP, exploring how AI coding agents are reshaping development—from IDEs to natural language commands—boosting productivity, cutting costs, and redefining how developers work with code.Guest speaker:Beyang Liu - CTO of SourcegraphHost:Demetrios Brinkmann - Founder of MLOps Community~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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/]
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A Candid Conversation with the CEO of Stack Overflow
AI Conversations Powered by Prosus Group Stack Overflow is adapting to the AI era by licensing its trusted Q&A corpus, expanding into discussions and enterprise tools, and reinforcing its role as a reliable source as developer trust in AI output declines.Guest speaker:Prashanth Chandrashekar - CEO of Stack OverflowHost:Demetrios Brinkmann - Founder of MLOps Community2025 Developer Survey: https://survey.stackoverflow.co/2025?utm_medium=referral&utm_source=direct-share&utm_campaign=dev-survey-2025&utm_content=MLOps~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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/]
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Knowledge is Eventually Consistent // Devin Stein // #335
Knowledge is Eventually Consistent // MLOps Podcast #335 with Devin Stein, CEO of Dosu.Grateful to @Databricks and @hyperbolic-labs for supporting our podcast and helping us keep great conversations going.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractAI as a partner in building richer, more accessible written knowledge—so communities and teams can thrive, endure, and expand their reach.// BioDevin is the CEO and Founder of Dosu. Prior to Dosu, Devin was an early engineer and leader at various startups. Outside of work, he is an active open source contributor and maintainer.// Related LinksWebsite: https://github.com/devsteinhttps://www.youtube.com/watch?v=sC8aW47DqPghttps://www.youtube.com/watch?v=PuM0Gd3txfQhttps://www.youtube.com/watch?v=ah6diDQ9wywhttps://www.youtube.com/watch?v=x22FEQic8lg~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Devin on LinkedIn: /devstein/Timestamps:[00:00] Devin's preferred coffee[00:53] Facts agent overview[03:47] Decision state detection[07:55 - 8:41] Databricks ad[08:42] Context-dependent word meanings [15:25] Fact lifecycle management[24:40] Maintaining quality documentation[30:10 - 31:06] Hyperbolic ad[31:07] Agent collaboration scenarios [38:22] Knowledge maintenance[44:10] Deployment and integration strategies[48:13] Flywheel data approach[51:54] Horror story engineering function[54:32] Wrap up
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