MLOps.community Podcast Por Demetrios arte de portada

MLOps.community

MLOps.community

De: Demetrios
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Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)Demetrios
Episodios
  • LLM Search, UI/UX challenges, Context Engineering and the 80/20 of Eval
    Sep 5 2025

    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 Group


    Host: Demetrios Brinkmann - Founder of MLOps Community


    ~~~~~~~~ ✌️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/]


    #contextengineering #aiengineer #aiinfrastructure #podcast

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    53 m
  • The Era of AI Agents in Marketing // Joel Horwitz // #337
    Sep 1 2025

    The Era of AI Agents in Marketing // MLOps Podcast #337 with Joel Horwitz, Growth Engineer at Neoteric3D.


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

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


    // Abstract

    We’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.


    // Bio

    Joel 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 Links

    Website: https://www.neoteric3d.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)]

    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 Joel on LinkedIn: /joelshorwitz


    Timestamps:

    [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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    49 m
  • Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336
    Aug 27 2025

    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

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    58 m
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