Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations.

// Bio

Arpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding.

She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita’s work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community.

Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI reflects both in her academic pursuits and professional contributions.

// Related Links

#recommendersystems #LLMs #linkedin

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Timestamps:

[00:00] Smarter Content Recommendations

[05:19] LLMs: Next-Gen Recommendations

[09:37] Judging LLM Suggestions

[11:38] Old vs New Recommenders

[14:11] Why LLMs Get Stuck

[16:52] When Old Models Win

[22:39] After-Booking Rec Magic

[23:26] One LLM to Rule Models

[29:14] Personalization That Evolves

[32:39] SIM Beats Transformers in QA

[35:35] Agents Writing Research Papers

[37:12] Big-Company Agent Failures

[41:47] LinkedIn Posts Fade Faster

[46:04] Clustering Shifts Social Feeds

[47:01] Vanishing Posts, Replay Mode

Agents in Production [Podcast Limited Series] Episode Nine – Training LLMs, Picking the Right Models, and GPU Headaches

Paul van der Boor and Zulkuf Genc from Prosus join Demetrios to talk about what it really takes to get AI agents running in production. From building solid eval sets to juggling GPU logistics and figuring out which models are worth using (and when), they share hard-won lessons from the front lines. If you're working with LLMs at scale—or thinking about it—this one’s for you.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Zulkuf Genc - Director of AI at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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Demetrios chats with Paul van der Boor and Bruce Martens from Prosus about the real bottlenecks in AI agent development—not tools, but evaluation and feedback. They unpack when to build vs. buy, the tradeoffs of external vendors, and how internal tools like Copilot are reshaping workflows.

Guest speakers:

Paul van der Boor - VP AI at Prosus Group

Bruce Martens - AI Engineer at Prosus Group

Host:

Demetrios Brinkmann - Founder of MLOps Community

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