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

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#contextengineering #aiengineer #aiinfrastructure #podcast

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

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// 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

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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

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

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// 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/

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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

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 Sourcegraph

Host:

Demetrios Brinkmann - Founder of MLOps Community

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

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 Overflow

Host:

Demetrios Brinkmann - Founder of MLOps Community

2025 Developer Survey: https://survey.stackoverflow.co/2025?utm_medium=referral&utm_source=direct-share&utm_campaign=dev-survey-2025&utm_content=MLOps

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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.

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// Abstract

AI as a partner in building richer, more accessible written knowledge—so communities and teams can thrive, endure, and expand their reach.

// Bio

Devin 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 Links

Website: https://github.com/devstein

https://www.youtube.com/watch?v=sC8aW47DqPg

https://www.youtube.com/watch?v=PuM0Gd3txfQ

https://www.youtube.com/watch?v=ah6diDQ9wyw

https://www.youtube.com/watch?v=x22FEQic8lg

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

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect 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

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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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Arpita on LinkedIn: /arpita-v-0a14a422/

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

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

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