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Demetrios

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

  1. LLMs at Scale: Infrastructure That Keeps AI Safe, Smart & Affordable // Marco Palladino// # 341

    4天前

    LLMs at Scale: Infrastructure That Keeps AI Safe, Smart & Affordable // Marco Palladino// # 341

    LLMs at Scale: Infrastructure That Keeps AI Safe, Smart & Affordable // MLOps Podcast #341 with Marco Palladino, Kong's Co-Founder and CTO. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract While conversations around AI regulations continue to evolve, the responsibility for AI continues to be with developers. In this episode, Marco Palladino, CTO and co-founder of Kong Inc., explores what it means to build and scale AI responsibly when the rulebook is still being written. He explains that infrastructure should be the frontline defense for enforcing governance, security, and reliability in AI deployments. Marco shares how Kong’s technologies, including AI Gateway and AI Manager, help organizations rein in shadow AI, reduce LLM hallucinations, improve observability, and act as the foundation for agentic workflows. // Bio Marco Palladino is an inventor, software developer, and internet entrepreneur. As the CTO and co-founder of Kong, he is Kong’s co-author, responsible for the design and delivery of the company’s products, while also providing technical thought leadership around APIs and microservices within both Kong and the external software community. Prior to Kong, Marco co-founded Mashape in 2010, which became the largest API marketplace and was acquired by RapidAPI in 2017. // Related Links Website: https://konghq.com/ https://www.youtube.com/watch?v=odpPVeQZjHU https://www.thestack.technology/the-big-interview-kong-cto-marco-palladino/ ~~~~~~~~ ✌️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 Marco on LinkedIn: /marcopalladino/ Timestamps: [00:00] Agent-mediated interactions shift [01:17] Kong connectivity and agents [04:36] Transcript cleanup request [08:11] MCP server use cases [12:37] Agent world possibilities [15:55] Business communication evolution [18:55] System optimization [25:36] AI gateway patterns [31:30] Investment decision making [35:54] Building conviction process [41:34] Polished customer conversation [46:37] AI gateway R&D future [50:52] Wrap up

    51 分钟
  2. On-Device AI Agents in Production: Privacy, Performance, and Scale // Varun Khare & Neeraj Poddar // #340

    9月30日

    On-Device AI Agents in Production: Privacy, Performance, and Scale // Varun Khare & Neeraj Poddar // #340

    On-Device AI Agents in Production: Privacy, Performance, and Scale // MLOps Podcast #340 with NimbleEdge's Varun Khare, Founder/CEO and Neeraj Poddar, Co-founder & CTO. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract AI agents are transitioning from experimental stages to performing real work in production; however, they have largely been limited to backend task automation. A critical frontier in this evolution is the on-device AI agent, enabling sophisticated, AI-native experiences directly on mobile and embedded devices. While cloud-based AI faces challenges like constant connectivity demands, increased latency, privacy risks, and high operational costs, on-device breaks through these trade-offs. We'll delve into the practical side of building and deploying AI agents with “DeliteAI”, an open-source on-device AI agentic framework. We'll explore how lightweight Python runtimes facilitate the seamless orchestration of end-to-end workflows directly on devices, allowing AI/ML teams to define data preprocessing, feature computation, model execution, and post-processing logic independently of frontend code. This architecture empowers agents to adapt to varying tasks and user contexts through an ecosystem of tools natively supported on Android/iOS platforms, handling all the permissions, model lifecycles, and many more. // Bio Varun Khare Varun is the Founder and CEO of NimbleEdge, an AI startup pioneering privacy-first, on-device intelligence. With an academic foundation in AI and neuroscience from UC Berkeley, MPI Frankfurt, and IIT Kanpur, Varun brings deep expertise at the intersection of technology and science. Before founding NimbleEdge, Varun led open-source projects at OpenMined, focusing on privacy-aware AI, and published research in computer vision. Neeraj Poddar Neeraj Poddar is the Co-founder and CTO at NimbleEdge. Prior to NimbleEdge, he was the Co-founder of Aspen Mesh, VP of Engineering at Solo.io, and led the Istio open source community. He has worked on various aspects of AI, networking, security, and distributed systems over the span of his career. Neeraj focuses on the application of open source technologies across different industries in terms of scalability and security. When not working on AI, you can find him playing racquetball and gaining back the calories spent playing by trying out new restaurants. // Related Links Website: https://www.nimbleedge.com/ https://www.nimbleedge.com/blog/why-ai-is-not-working-for-you https://www.nimbleedge.com/blog/state-of-on-device-ai https://www.youtube.com/watch?v=Qqj_Nl2MihE https://www.linkedin.com/events/7343237917982527488/comments/ ~~~~~~~~ ✌️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 Varun on LinkedIn: /vkkhare/ Connect with Neeraj on LinkedIn: /nrjpoddar/ Timestamps: [00:00] On-device AI skepticism [02:47] Word suggestion for AI [06:40] Optimizing unique challenges [13:39] LLM on-device challenges [20:34] Agent overlord tension [23:56] AI app constraints [29:23] Siri limitations and trust gap [32:01] Voice-driven app privacy [35:49] Platform lock-in vs aggregation [42:26] On-device AI optimizations [45:38] Wrap up

    46 分钟
  3. The DuckLake Lakehouse Format // Hannes Mühleisen // #339

    9月19日

    The DuckLake Lakehouse Format // Hannes Mühleisen // #339

    The DuckLake Lakehouse Format // MLOps Podcast #339 with Hannes Mühleisen, Co-founder and CEO of DuckDB Labs. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Managing data on Object Stores has been a painful affair. Users had to choose between data swamp chaos or a maze of metadata files with catalog servers on top. DuckLake is a new paradigm for managing data on object stores: First, it uses classical SQL data management systems to manage metadata. Second, actual data is stored in Parquet files on pretty arbitrary storage. Third, processing queries is done client-side, or anywhere really. DuckDB is the first system to integrate with DuckLake using an extension with the same name. Conceptually, DuckLake enables central control over truth while decentralizing compute and storage entirely. DuckLake turns data warehouse architecture upside down by departing from the integrated metadata/compute layer towards a fully disconnected operation with only centralized metadata. For the first time, DuckLake allows a “multi-player” experience with DuckDB, where computation stays fully local, but transactional control is centralized. // Bio Hannes Mühleisen 🔈 is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs. He is a senior researcher at the Centrum Wiskunde & Informatica (CWI) in Amsterdam. He is also Professor of Data Engineering at Radboud University Nijmegen. // Related Links Website: https://hannes.muehleisen.orgUnleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 - https://youtu.be/pF8zTI867EI ~~~~~~~~ ✌️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 Hudson on LinkedIn: /hfmuehleisen Timestamps: [00:00] Spooky ease in tech [00:29] DuckDB and DuckLake [07:50] Pain vs trust factors [13:12] Prioritizing project features [16:16] Platform growth tension [22:06] Building principles [25:26] OSS vs system reliability [30:27] Creative uses of DuckDB [35:35] Tecton product strategy [43:30] Mindset shift [52:25] DuckDB future shifts [55:37] Wrap up

    57 分钟
  4. Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // #338

    9月9日

    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/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract For 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. // Bio Hudson 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 Links https://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg ~~~~~~~~ ✌️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 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 分钟
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Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

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