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MLOps.community

Demetrios
MLOps.community
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  • MLOps.community

    Architecting Modern AI Systems: Platforms, Agents, and Integration

    28/05/2026 | 56 min
    BuzzHPC Roundtable episode: Architecting Modern AI Systems: Platforms, Agents, and Integration

    Join the Community: https://go.mlops.community/YTJoinIn
    Get the newsletter: https://go.mlops.community/YTNewsletter
    MLOps GPU Guide: https://go.mlops.community/gpuguide

    Big shout-out to BuzzHPC for the collaboration!

    // Abstract
    As AI systems evolve into more autonomous, agent-driven architectures, the way we design platforms, tools, and infrastructure is rapidly changing. In this session with BuzzHPC, we explore the shifting boundary between platforms and tools, what developers expect platform providers to handle versus what they want to control and build themselves.

    We unpack what modern agentic stacks look like today, how teams are structuring them in production, and where these architectures are heading as systems become more complex and distributed. A key focus will also be on agent interoperability, how different agents communicate, coordinate, and operate within shared environments.

    Finally, we share insights and lessons from a recent AI hackathon delivered in partnership with Bell, Buzz, Mila, and KHP, highlighting how these concepts are being tested and applied by builders in real-world scenarios.

    // Bio
    Allen Roush
    Allen has held senior technical and AI leadership roles at companies like Oracle and Intel. He's very active in the AI research space and open source communities. He's passionate about improving the creativity and coherence of AI systems.

    Frédéric Bénard
    Frédéric is Senior Director of AI Applications Development at Mila (Quebec AI Institute), where he leads a team focused on building the engineering foundations for applied AI systems. His work centers on translating cutting-edge research into scalable applications, including AI-driven platforms and agent-based systems used across research and industry collaborations.

    Shuo Wang
    Shuo leads the Responsible AI Office for Bell Canada, where all AI use cases are reviewed and assessed for potential harm and bias. Previously, he led a team of data scientists to expand a large-scale ML program to improve customer support effectiveness.

    // Related Links
    Website: https://www.buzzhpc.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 Allen on LinkedIn: /allen-roush-27721011b/
    Connect with Frédéric on LinkedIn: /benard/
    Connect with Shuo on LinkedIn: /shuow/
  • MLOps.community

    [Special Announcement] MLOps Community Linux Foundation

    28/05/2026 | 2 min
    Big news: the MLOps Community is joining the Linux Foundation to become the official user community of the new Agentic AI Foundation (AAIF).
    The AAIF is the neutral home for open source projects like the Model Context Protocol (MCP), goose, and AGENTS.md, co-founded by Anthropic, Block, and OpenAI. With that governance and scaffolding now in place, the open source agent ecosystem has room to scale, and the MLOps Community is right in the middle of it.

    Everything you love about the community from the past six years keeps going, and we are adding even more on top.

    What this means:

    - Official user community: MLOps Community becomes the user community of the Agentic AI Foundation under the Linux Foundation.
    - The projects: MCP, goose, and AGENTS.md now live under one open, neutral governance structure built to scale.
    - Nothing goes away: The podcast, the global meetups, the weekly newsletter, the Slack workspace, and the virtual events all continue.
    - New: Ambassador Program: Just opened for applications, so you can get more involved in the community.
    - AgentCon EU: September 17 and 18 in Amsterdam.
    - AgentCon North America: October 22 and 23 in San Jose.
    - A possible new name: The podcast may become "Agentic Conversations," because honestly all we talk about is agents. Tell me what you think in the comments.

    If you build with AI agents or follow the open source agent ecosystem, this is the update to bookmark. This is MLOps Community 2.0.

    Links and Resources:
    - MLOps Community: https://mlops.community
    - MLOps Community 2.0: https://mlops.community/blog/mlops-community-2-0
    - Agentic AI Foundation: https://aaif.io
    - Ambassadors: https://aaif.io/ambassadors
    - Linux Foundation AAIF announcement: https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation
    - AgentCon and MCPCon events: https://events.linuxfoundation.org/aaif-events/
    - Model Context Protocol (MCP): https://modelcontextprotocol.io
    - goose: https://goose-docs.ai
    - AGENTS.md: https://agents.md

    Timestamps (approximate, adjust before publishing):
    00:00 The big announcement
    00:12 Joining the Linux Foundation's Agentic AI Foundation
    00:30 Why it matters: MCP, goose, and AGENTS.md
    00:48 What is not changing: podcast, meetups, newsletter, Slack
    01:15 What is new: the Ambassador Program
    01:30 AgentCon EU in Amsterdam and North America in San Jose
    01:55 A new name for the podcast: Agentic Conversations?
    02:10 MLOps Community 2.0

    #AgenticAI #MCP #LinuxFoundation
  • MLOps.community

    Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce

    26/05/2026 | 1 h 18 min
    Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation) from Just Eat Takeaway.com join the MLOps.community to pull back the curtain on how one of Europe's largest food delivery platforms is running an internal innovation engine. From autonomous delivery robots to agentic AI voice assistants, they share what it actually takes to build like a startup inside a 40,000-person company.

    Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce // MLOps Podcast #377 with Just Eat Takeaway.com's Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation)

    🤖 Delivery Robots — How JET partnered with RIVR and DELIVERS.AI to deploy physical AI ground robots in Zurich, Milton Keynes, and Bristol, and what the first pilots taught the team
    🧠 AI Incubation at Scale — How Nidhi's team built a dedicated incubation unit to fast-track AI experiments without the red tape of a large enterprise
    🎙️ AI Voice Assistant — The story behind JET's new voice-first food ordering experience, and the ML challenges of building a conversational concierge at scale
    🦾 Physical AI vs. Software AI — Why deploying wheeled-legged robots in real cities is fundamentally different from shipping a model update, and the MLOps implications
    🚀 Corporate Innovation Playbook — The frameworks Guthrie and Nidhi use to move from idea to pilot in weeks, not quarters, inside a large org
    📦 Innovation as a Platform — How JET is thinking about turning its delivery infrastructure and AI capabilities into a reusable platform for new business lines
    🔗 Startup Partnerships — What makes a good external innovation partner (vs. building in-house), and how JET evaluates robotics and AI startups for pilots
    ⚡ Agentic AI & Accessibility — How agentic AI is being used to make food ordering genuinely accessible for blind and low-vision users

    Whether you're an ML engineer at a large company trying to get AI into production, a product leader navigating corporate innovation, or a startup founder looking to partner with a platform player — this conversation is packed with practical lessons.

    🔗 Links & Resources:
    Just Eat Takeaway.com: https://www.justeattakeaway.com
    RIVR (physical AI delivery robots): https://www.rivr.ai
    DELIVERS.AI (UK delivery robots): https://www.delivers.ai
    Prosus (JET parent company): https://www.prosus.com
    MLOps.community: https://mlops.community

    ⏱️ Timestamps
    [00:00] AI Innovation Incubator Strategy
    [03:16] Everyday Convenience Expansion
    [07:03] Context Ownership in Ecosystems
    [17:35] LLM Integration and Discovery
    [24:02] Whoop Notifications Grievances
    [33:01] Expanding Beyond Food
    [48:20] Innovation Lab Failures
    [51:22] Rory Sutherland's Alchemy
    [1:03:23] Latency and Conversational Design
    [1:13:42] Drone Delivery Efficiency
    [1:18:06] Wrap up

    #AgenticCommerce #VoiceAI #DroneDelivery
  • MLOps.community

    Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality

    19/05/2026 | 42 min
    Pramod Krishnan is a Managing Director - AI Managed Services at PwC, specializing in enterprise AI transformation — helping large organizations move from AI experimentation to production operating models. In this episode with Demetrios, Pramod breaks down exactly what the OpenClaw wave means for enterprises, and the control frameworks PwC uses before a single agent touches production.

    Huge thanks to ⁠PwC⁠ for supporting this episode!

    Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality // MLOps Podcast #378 with Pramod Krishnan, Managing Director - AI Managed Services at PwC US.

    🔑 OpenClaw & the Agentic Hype Cycle — Why the fastest-growing open-source agent project in history (190K+ GitHub stars in weeks) is a forcing function for enterprise AI governance, and what most organizations are getting wrong.
    🏗️ 3-Tier Work Classification — Pramod's framework for categorizing any agentic task as reversible, sensitive, or consequential — and how the approval gates, controls, and blast radius differ for each tier.
    🛡️ The Guardrails Stack — A concrete list of non-negotiable guardrails: allow-listed tool calls, prompt injection defense, credential protection, toxic output filtering, and more — straight from PwC's production deployments.
    🔍 5-Part Auditability Framework — How to make AI agents truly auditable across quality (LLM-as-judge), performance, safety, cost, and security — and why OpenTelemetry alone isn't enough.
    💰 Agent Cost & ROI Tracking — Why successfully deployed agents are generating the hardest financial measurement problems enterprises have ever faced, and what a real cost-tracking architecture looks like.
    🔒 Agent Security in Depth — From API key harvesting attacks to credential leakage to malicious actor scenarios: what security controls PwC requires before any agent goes live.
    ⚙️ The Minimum Control Stack — The non-negotiables Pramod would walk in with on a Monday before clearing any agent for production: what they are, why they matter, and how to implement them.
    🔄 Human-in-the-Loop Design — The difference between "human in the loop" (approves every action) and "human on the loop" (monitors and intervenes) — and how to choose the right pattern based on consequence level.
    🤝 AI as a Force Multiplier — How Pramod thinks about AI ownership, intellectual authorship, and making sure humans remain deliberate and responsible even as agents accelerate output.

    This episode is essential for ML engineers, platform architects, CIOs, and AI product managers who are moving beyond demos into real enterprise agentic deployments.

    🔗 Links & ResourcesPramod Krishnan on LinkedIn: https://www.linkedin.com/in/pramod-potti-krishnan/
    MLOps.community: https://mlops.community
    OpenClaw project: https://openclaw.ai
    BCG on OpenClaw + Enterprise: https://www.bcg.com/publications/cios-openclaw-and-the-new-wave-of-ai-agents
    PwC 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

    Timestamps:
    [00:00] AI in Enterprise
    [02:04] AI System Failures
    [08:01] Agent Decision Tracing
    [13:07] Agent Design Tension
    [16:21] Agent Control Stack Essentials
    [20:20] LLM Cost and FinOps
    [26:16] Agent Attack Surfaces
    [30:00] Tools as Attack Vectors
    [33:47] Human in the Loop
    [37:00] AI Ownership and Accountability
    [41:42] Wrap up. Shoutout to Pramod and PwC!
  • MLOps.community

    Agents are Just While Loops

    15/05/2026 | 41 min
    Hamza Tahir, co-founder of ZenML, joins the show to cut through the hype around long-running agents — arguing that at the end of the day, an agent is just a while loop that talks to a model, calls a tool, and writes to a file system. He covers the architecture of agent harnesses (inner and outer), what durable execution actually guarantees (and what it doesn't), and why the ML pipeline paradigm is a cleaner mental model than transactions for most agent workloads.

    Hamza also announces Kitaru — ZenML's new open-source execution runtime for async Python agents — built on five years of running ML workloads in enterprise environments.

    What we get into:
    Agents are while loops: The surprising simplicity under all the tooling: a brain (LLM), hands (tool calls), and a file system, stacked recursively
    Inner harness vs outer harness: Why Pydantic AI owns the inner loop while production deployment needs a separate runtime layer
    What "long-running" actually means: Why the infrastructure we need to build is about extrapolating the future, not defining a time window today
    Durable execution demystified: What checkpointing actually guarantees (infra failures, pod death, network drops) vs. what it never will (external state, bad LLM outputs, Snowflake rollbacks)
    ML pipelines vs transactions: Why bursty containers in Kubernetes map more naturally to agent workloads than microsecond-latency queue workers — and why Hamza argues against the complexity tax
    Anthropic opening the harness: Why letting other models run Claude Cowork is a "boss move," and what it means for the one-harness vs one-model debate
    Human-in-the-loop, done right: The pod-kill-and-resume pattern, and why warm pools matter less when your agent runs for days
    Kitaru: ZenML's new open source durable execution runtime: zero-config local, Kubernetes/SageMaker/Vertex in production, built on Pydantic AI integration
    Arguing with Claude about Temporal: Hamza's story of spending hours getting an LLM to admit ZenML and Temporal solves the same problem

    If you're architecting agents for production, picking between Pydantic AI, LangGraph, and Temporal, or just want to understand what "durable execution" actually means — this is the episode.

    // LINKS & RESOURCES
    Kitaru on GitHub: https://github.com/zenml-io/kitaru
    Kitaru launch blog post: https://www.zenml.io/blog/kitaru-launch
    Kitaru on Hacker News: https://news.ycombinator.com/item?id=47520115
    Hamza Tahir on LinkedIn: https://www.linkedin.com/in/hamzatahirofficial/
    ZenML: https://www.zenml.io/

    Timestamps
    [00:00] While Loop Checkpointing
    [00:24] Long-Running Agents Explained
    [01:28] Agent Harness Model Definitions
    [06:30] Durability and State Recovery
    [11:03] Agent Systems Layers
    [18:45] Durability in Agent Systems
    [22:07] ML Pipeline vs Transactions
    [29:23] Durability vs Guarantees
    [33:13] Durability vs Chaos Engineering
    [39:50] Kitaru Naming and Purpose
    [40:38] Wrap up

    #AIAgents #DurableExecution #OpenSource
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