Economics of Building Data Centers, GPU Clouds, Sovereign AI
AI Conversations Powered by Prosus Group. Craig Tavares, COO of Buzz High Performance Compute, shares lessons from building GPU cloud infrastructure worldwide. He stresses the role of sovereign mandates, renewable power, and modular cooling in scaling data centers, while highlighting NVIDIA partnerships and orchestration as key to sustaining AI workloads.Guest speaker: Craig Tavares - COO of Buzz HPCHost: 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/]
--------
45:59
--------
45:59
Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // #338
Trust at Scale: Security and Governance for Open Source Models // MLOps Podcast #338 with Hudson Buzby, Solutions Architect at JFrog.Appreciate JFrog for their support in bringing this blog to life.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractFor better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation, we will discuss ways that enterprise-scale organizations are addressing these challenges to safely and securely build these development environments. // BioHudson Buzby is a solution engineer with an emphasis on MLOps, LLMOps, Big Data, and Distributed Systems, leveraging his expertise to help organizations optimize their machine learning operations and large language model deployments. His role involves providing technical solutions and guidance to enhance the efficiency and effectiveness of AI-driven projects.// Related Linkshttps://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg~~~~~~~~ ✌️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 Hudson on LinkedIn: /hudson-buzby/Timestamps:[00:00] Value of Centralized Gateway[00:35] Point Break vs Big Lebowski[01:47] AI adoption failure stats[05:12] ML vs Generative AI[12:04] LLM adoption in enterprise[18:08] MLOps Community alternative[23:43] AI governance challenges[27:39] Organizational debt comparison[31:41] AI tool sprawl[35:59] MLOps to platform evolution[40:56] MLOps then vs now[49:48] Model trust and safety[52:19] AI model effectiveness[55:54] Product discovery process[58:38] Wrap up
--------
59:22
--------
59:22
LLM Search, UI/UX challenges, Context Engineering and the 80/20 of Eval
AI Conversations Powered by Prosus Group Nishikant Dhanuka talks about what it really takes to make AI agents useful—especially in e-commerce and productivity. From making them smarter with context (like user history and real-time data) to mixing chat and UI for smoother interactions, he breaks down what’s working and what’s not. He also shares why evals matter, how to test with real users, and why AI only succeeds when it actually makes life easier, not more complicated.Guest speaker: Nishikant Dhanuka - Senior Director of AI at Prosus GroupHost: 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/]#contextengineering #aiengineer #aiinfrastructure #podcast
--------
52:36
--------
52:36
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
--------
48:56
--------
48:56
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
Portugal