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    Technology

    Agentic Conversations (formally mlops.community)

    Relaxed conversations and technical deep dives around AI Agents. This Show is brought to you by the Agentic AI Foundation where the leading agentic open-source projects like MCP, Agents.md, and Goose live. See more at aaif.io

    Advertise

    Copyright: © Demetrios Brinkmann

    • Apple Podcasts
    • Google Play
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    Latest Episodes:
    Talk to Your Data: The SQL Data Analyst Feb 28, 2025
    Show notes

    In Agents in Production [Podcast Limited Series] - Episode Four, Donné Stevenson and Paul van der Boor break down the deployment of a Token Data Analyst agent at Prosus—why, how, and what worked. They discuss the challenges of productionizing the agent, from architecture to mitigating LLM overconfidence, key design choices, the role of pre-checks for clarity, and why they opted for simpler text-based processes over complex recursive methods.


    Guest speakers:

    Paul van der Boor - VP AI at Prosus Group

    Donne Stevenson - Machine Learning Engineer 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/]

    Connect with Demetrios on LinkedIn: /dpbrinkm


    Getting to Grips with Web Agents Feb 26, 2025
    Show notes

    Agents in Production [Podcast Limited Series] - Episode Three explores the concept of web agents—AI-powered systems that interact with the web as humans do, navigating browsers instead of relying solely on APIs.

    The discussion covers why web agents emerge as a natural step in AI evolution, their advantages over API-based systems, and their potential impact on e-commerce and automation.

    The conversation also highlights challenges in making websites agent-friendly and envisions a future where agents seamlessly handle tasks like booking flights or ordering food.


    Guest speakers:

    Paul van der Boor - VP AI at Prosus Group

    Chiara Caratelli - Data Scientist 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/]

    Connect with Demetrios on LinkedIn: /dpbrinkm


    The Challenge with Voice Agents Feb 22, 2025
    Show notes

    In Agents in Production Series - Episode Two, Demetrios, Paul, and Floris explore the latest in Voice AI agents. They discuss real-time voice interactions, OpenAI's real-time Voice API, and real-world deployment challenges. Paul shares insights from iFood’s voice AI tests in Brazil, while Floris highlights technical hurdles like turn detection and language processing. The episode covers broader applications in healthcare and customer service, emphasizing continuous learning and open-source innovation in Voice AI.


    Guest speakers:

    Paul van der Boor - VP AI at Prosus Group

    Floris Fok - AI Engineer 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/]

    Connect with Demetrios on LinkedIn: /dpbrinkm


    The Agent Landscape - Lessons Learned Putting Agents Into Production Feb 20, 2025
    Show notes

    In Agents in Production Series - Episode One, Demetrios chats with Paul van der Boor and Floris Fok about the real-world challenges of deploying AI agents across @ProsusGroup of companies. They break down the evolution from simple LLMs to fully interactive systems, tackling scale, UX, and the harsh lessons from failed projects. Packed with insights on what works (and what doesn’t), this episode is a must-listen for anyone serious about AI in production.


    Guest speakers:

    Paul van der Boor - VP AI at Prosus Group

    Floris Fok - AI Engineer 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/⁠]

    Connect with Demetrios on LinkedIn: ⁠/dpbrinkm


    Evolving Workflow Orchestration // Alex Milowski // #291 Feb 14, 2025
    Show notes

    Alex Milowski is a researcher, developer, entrepreneur, mathematician, and computer scientist.

    Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist.


    // Abstract

    There seems to be a shift from workflow languages to code, mostly annotation Python - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs, and at MicroByre, we used an external workflow language. Both had a batch task executor on K8S, but at MicroByre, we had human and robot in the loop workflows.


    // Bio

    Dr. Milowski is a serial entrepreneur and computer scientist with experience in a variety of data and machine learning technologies. He holds a PhD in Informatics (Computer Science) from the University of Edinburgh, where he researched large-scale computation over scientific data. Over the years, he's spent many years working on various aspects of workflow orchestration in industry, standardization, and research.


    // Related Links

    Website: https://www.milowski.com/


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community: https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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

    // MLOps Swag/Merch

    ⁠https://shop.mlops.community/⁠


    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

    Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexmilowski/


    Timestamps:

    [00:00] Alex's preferred coffee

    [00:20] Takeaways

    [01:27] Workflow Systems Overview

    [05:54] Everything as a DAG

    [09:25] Workflows, Layering, and Ownership

    [12:46] Workflow Awareness for Success

    [17:31] Process Engineering Challenges

    [21:20] ML Workflow Trends

    [27:57] Data Engineering and ML Tools

    [33:18] Infrastructure as Code Pros and Cons

    [36:20] DSLs in Organizations

    [43:04] Agents vs DAGs

    [47:48] Agent Workflow Selection

    [50:31] RPA Business Automation Tools

    [55:00] LLM Integration in BPA

    [1:01:14] Diversity in Decision Workflows

    [1:03:09] Shadow AI Governance Risks

    [1:08:34] Centralized ML Workflow Standardization

    [1:12:50] Wrap up


    Insights from Cleric: Building an Autonomous AI SRE // Willem Pienaar // #290 Feb 11, 2025
    Show notes

    Willem Pienaar is the Co-Founder and CTO of Cleric. He previously worked at Tecton as a Principal Engineer. Willem Pienaar attended the Georgia Institute of Technology.

    Insights from Cleric: Building an Autonomous AI SRE // MLOps Podcast #289 with Willem Pienaar, CTO & Co-Founder of Cleric.


    // Abstract

    In this MLOps Community Podcast episode, Willem Pienaar, CTO of Cleric, breaks down how they built an autonomous AI SRE that helps engineering teams diagnose production issues. We explore how Cleric builds knowledge graphs for system understanding and uses existing tools/systems during investigations. We also get into some gnarly challenges around memory, tool integration, and evaluation frameworks, and some lessons learned from deploying to engineering teams.


    // Bio

    Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories. Before starting Cleric, Willem led the open-source engineering team at Tecton and established the ML platform team at Gojek, where he built high-scale ML systems for the Southeast Asian Decacorn.


    // Related Links

    Website: willem.co


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community:

    https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup:

    https://go.mlops.community/register

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

    // MLOps Swag/Merch⁠

    https://shop.mlops.community/⁠


    Connect with Demetrios on LinkedIn:

    https://www.linkedin.com/in/dpbrinkm/

    Connect with Willem on LinkedIn:

    https://www.linkedin.com/in/willempienaar/


    Timestamps:

    [00:00] Willem's preferred coffee

    [00:18] Takeaways

    [02:28] AI SRE Challenges

    [06:07] Complexity in Knowledge Graphs

    [16:25] Agent Budget Loops

    [20:07] AI Knowledge Graph Triage

    [24:21] Memory in AI Agents

    [31:32] Alert Fatigue and UX

    [38:21] Pricing for Agent Solutions

    [41:34] Tool Integration Challenges

    [45:52] Agent Root Cause Analysis

    [50:56] True Resolution Challenges

    [55:20] Wrap up


    Robustness, Detectability, and Data Privacy in AI // Vinu Sankar Sadasivan // #289 Feb 07, 2025
    Show notes

    Vinu Sankar Sadasivanis a CS PhD ... Currently, I am working as a full-time Student Researcher at Google DeepMind on jailbreaking multimodal AI models.


    Robustness, Detectability, and Data Privacy in AI // MLOps Podcast #289 with Vinu Sankar Sadasivan, Student Researcher at Google DeepMind.


    // Abstract

    Recent rapid advancements in Artificial Intelligence (AI) have made it widely applicable across various domains, from autonomous systems to multimodal content generation. However, these models remain susceptible to significant security and safety vulnerabilities. Such weaknesses can enable attackers to jailbreak systems, allowing them to perform harmful tasks or leak sensitive information. As AI becomes increasingly integrated into critical applications like autonomous robotics and healthcare, the importance of ensuring AI safety is growing. Understanding the vulnerabilities in today’s AI systems is crucial to addressing these concerns.


    // Bio

    Vinu Sankar Sadasivan is a final-year Computer Science PhD candidate at The University of Maryland, College Park, advised by Prof. Soheil Feizi. His research focuses on Security and Privacy in AI, with a particular emphasis on AI robustness, detectability, and user privacy. Currently, Vinu is a full-time Student Researcher at Google DeepMind, working on jailbreaking multimodal AI models. Previously, Vinu was a Research Scientist intern at Meta FAIR in Paris, where he worked on AI watermarking.


    Vinu is a recipient of the 2023 Kulkarni Fellowship and has earned several distinctions, including the prestigious Director’s Silver Medal. He completed a Bachelor’s degree in Computer Science & Engineering at IIT Gandhinagar in 2020. Prior to their PhD, Vinu gained research experience as a Junior Research Fellow in the Data Science Lab at IIT Gandhinagar and through internships at Caltech, Microsoft Research India, and IISc.


    // MLOps Swag/Merch

    https://shop.mlops.community/


    // Related Links

    Website: https://vinusankars.github.io/


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community: https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

    Connect with Richard on LinkedIn: https://www.linkedin.com/in/vinusankars/


    Timestamps:

    [00:00] Vinu's preferred coffee

    [00:31] Takeaways

    [01:09] AI Detection Limitations

    [05:20] AI Text Disclosure Ethics

    [14:05] Watermarking AI Models

    [25:04] Threshold Trade-offs Explained

    [29:41] Red Teaming AI Evolution

    [36:30] Adversarial Prompt Optimization

    [41:41] Model Strengths and Weaknesses

    [47:57] Wrap up


    AI & Aliens: New Eyes on Ancient Questions // Richard Cloete // #288 Feb 04, 2025
    Show notes

    Richard Cloete is a computer scientist and a Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. He is a member of the Galileo Project working under the supervision of Professor Avi, having recently held a postdoctoral position at the University of Cambridge, UK.


    AI & Aliens: New Eyes on Ancient Questions // MLOps Podcast #288 with Richard Cloete, Laukien-Oumuamua Postdoctoral Research Fellow at Harvard University.


    // Abstract

    Demetrios speaks with Dr. Richard Cloete, a Harvard computer scientist and founder of SEAQR Robotics, about his AI-driven work in tracking Unidentified Aerial Phenomena (UAPs) through the Galileo Project. Dr. Cloete explains their advanced sensor setup and the challenges of training AI in this niche field, leading to the creation of AeroSynth, a synthetic data tool.


    He also discusses his collaboration with the Minor Planet Center on using AI to classify interstellar objects and upcoming telescope data. Additionally, he introduces Seeker Robotics, applying similar AI techniques to oceanic research with unmanned vehicles for marine monitoring. The conversation explores AI’s role in advancing our understanding of space and the ocean.


    // Bio

    Richard is a computer scientist and Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. As a member of the Galileo Project under Professor Avi Loeb's supervision, he develops AI models for detecting and tracking aerial objects, specializing in Unidentified Anomalous Phenomena (UAP). Beyond UAP research, he collaborates with astronomers at the Minor Planet Center to create AI models for identifying potential interstellar objects using the upcoming Vera C. Rubin Observatory. Richard is also the CEO and co-founder of SEAQR Robotics, a startup developing advanced unmanned surface vehicles to accelerate the discovery of novel life and phenomena in Earth's oceans and atmosphere. Before joining Harvard, he completed a postdoctoral fellowship at the University of Cambridge, UK, where his research explored the intersection of emerging technologies and law.


    Grew up in Cape Town, South Africa, where I used to build Tesla Coils, plasma globes, radio stethoscopes, microwave guns, AM radios, and bombs...


    // MLOps Swag/Merch

    https://shop.mlops.community/


    // Related Links

    Website: www.seaqr.nethttps://itc.cfa.harvard.edu/people/richard-cloete


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community: https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

    Connect with Richard on LinkedIn: https://www.linkedin.com/in/richard-cloete/


    Timestamps:

    [00:00] Richard's preferred coffee

    [00:16] Takeaways

    [01:42] AI for Human-Centered Design

    [09:10] AI Storm Tracking Use Case

    [13:34] Edge Computing Workflow

    [17:18] UAP AI Space Exploration

    [25:24] Seeker Robotics Exploration

    [36:25] Data Demand and Use Cases

    [43:57] Unexpected Movement Patterns

    [46:15] Wrap up


    Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287 Jan 31, 2025
    Show notes

    A software engineer based in Delft, Alex Strick van Linschoten, recently built Ekko, an open-source framework for adding real-time infrastructure and in-transit message processing to web applications. With years of experience in Ruby, JavaScript, Go, PostgreSQL, AWS, and Docker, I bring a versatile skill set to the table. I hold a PhD in History, have authored books on Afghanistan, and currently work as an ML Engineer at ZenML.


    Real LLM Success Stories: How They Actually Work // MLOps Podcast #287 with Alex Strick van Linschoten, ML Engineer at ZenML.


    // Abstract

    Alex Strick van Linschoten, a machine learning engineer at ZenML, joins the MLOps Community podcast to discuss his comprehensive database of real-world LLM use cases. Drawing inspiration from Evidently AI, Alex created the database to organize fragmented information on LLM usage, covering everything from common chatbot implementations to innovative applications across sectors. They discuss the technical challenges and successes in deploying LLMs, emphasizing the importance of foundational MLOps practices. The episode concludes with a call for community contributions to further enrich the database and collective knowledge of LLM applications.


    // Bio

    Alex is a Software Engineer based in the Netherlands, working as a Machine Learning Engineer at ZenML. He was previously awarded a PhD in History (specialism: War Studies) from King's College London and has authored several critically acclaimed books based on his research work in Afghanistan.


    // MLOps Swag/Merch

    https://shop.mlops.community/


    // Related Links

    Website: https://mlops.systems

    https://www.zenml.io/llmops-databasehttps://www.zenml.io/llmops-database

    https://www.zenml.io/blog/llmops-in-production-457-case-studies-of-what-actually-works

    https://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llms

    https://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementations

    https://huggingface.co/datasets/zenml/llmops-database


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community: https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

    Connect with Alex on LinkedIn: https://www.linkedin.com/in/strickvl


    Timestamps:

    [00:00] Alex's preferred tea

    [00:15] Takeaways

    [00:55] LLM Database Creation Insights

    [03:26] Hidden Gems and LLMs

    [07:04] Chatbot Governance Challenges

    [15:16] AI Agents and IPOs

    [19:51] AI Interface Evolution

    [23:56] LLMs as Product Guides

    [26:57] RAG with User Context

    [30:29] User Experience Friction Points

    [36:20] ROI and Engineering Insights

    [41:09] Agent Debugging and Flows

    [45:28] Data Viz Ideas LLM

    [47:41] Wrap up


    Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 Jan 27, 2025
    Show notes

    In his 13 years of software engineering, Ilya Reznik has specialized in commercializing machine learning solutions and building robust ML platforms. He's held technical lead and staff engineering roles at premier firms like Adobe, Twitter, and Meta. Currently, Ilya channels his expertise into his travel startup, Jaunt, while consulting and advising emerging startups.


    Navigating Machine Learning Careers: Insights from Meta to Consulting // MLOps Podcast #286 with Ilya Reznik, ML Engineering Thought Leader at Instructed Machines, LLC.


    // Abstract

    Ilya Reznik's insights into machine learning and career development within the field. With over 13 years of experience at leading tech companies such as Meta, Adobe, and Twitter, Ilya emphasizes the limitations of traditional model fine-tuning methods. He advocates for alternatives like prompt engineering and knowledge retrieval, highlighting their potential to enhance AI performance without the drawbacks associated with fine-tuning.


    Ilya's recent discussions at the NeurIPS conference reflect a shift towards practical applications of Transformer models and innovative strategies like curriculum learning. Additionally, he shares valuable perspectives on navigating career progression in tech, offering guidance for aspiring ML engineers aiming for senior roles. His narrative serves as a blend of technical expertise and practical career advice, making it a significant resource for professionals in the AI domain.


    // Bio

    Ilya has navigated a diverse career path since 2011, transitioning from physicist to software engineer, data scientist, ML engineer, and now content creator. He is passionate about helping ML engineers advance their careers and making AI more impactful and beneficial for society.


    Previously, Ilya was a technical lead at Meta, where he contributed to 12% of the company’s revenue and managed approximately 30 production ML models. He also worked at Twitter, overseeing offline model evaluation, and at Adobe, where his team was responsible for all intelligent services within Adobe Analytics.


    Based in Salt Lake City, Ilya enjoys the outdoors, tinkering with Arduino electronics, and, most importantly, spending time with his family.


    // MLOps Swag/Merch

    https://shop.mlops.community/

    // Related Links

    Website: mlepath.com


    --------------- ✌️Connect With Us ✌️ -------------

    Join our Slack community: https://go.mlops.community/slack

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

    Connect with Ilya on LinkedIn: https://www.linkedin.com/in/ibreznik/


    Timestamps:

    [00:00] Ilya's preferred coffee

    [00:12] Takeaways

    [01:10] Fine-tuning: Pros and Cons

    [02:26] Fine-tuning Gamble

    [07:50] LLM Architecture Tradeoffs

    [11:37] NeurIPS Takeaways and Insights

    [22:42] AI Benchmarking and Data Leaks

    [29:30] Staff ML Engineer Path

    [40:08] Staff Engineer Titles Explained

    [49:00] E6 to E7 Expectations

    [51:43] Tech Jobs vs Startups

    [55:33] Senior to Staff Journey

    [58:49] Wrap up


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