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MLOps.community

Demetrios
MLOps.community
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526 episodios

  • MLOps.community

    Logs Are All You Need: Rethinking Observability with AI Agents

    02/06/2026 | 46 min
    Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company — now he's back, betting that logs are all you need to replace Datadog.

    Logs Are All You Need: Rethinking Observability with AI Agents // MLOps Podcast #381 with Sherwood Callaway, the Founder of Sazabi

    🔑 What's covered:
    🪵 Logs vs. The Three Pillars — Sherwood makes the case that the traditional observability stack (metrics, logs, traces) is overkill. In 2026, with AI agents in the loop, logs alone are sufficient — and dramatically simpler to instrument.
    🚨 AI-Generated Alerts, Not AI-Evaluated Alerts — Instead of using AI to triage your noisy alert stream, Sazabi generates the alerts autonomously from your logs and codebase — so you never configure a monitor again.
    🤖 Agent Sandboxing & Bash Access — How Sazabi gives its AI agent a persistent bash sandbox with CLI tool access, why every other action routes through that sandbox, and how RLS database permissions keep the agent from doing damage.
    🧠 Agentic Memory via Git — Sazabi's novel approach to persisting agent memory across threads using Git branches — enabling multiple parallel sub-agents to share findings without bloating the context window.
    🔀 Multi-Agent Parallelization — How Sazabi spawns sub-agents and background agents on-demand to investigate production issues in parallel, the way Claude Code displays a live to-do list of agent work.
    📊 Why Evals Are Hard (and What They Built Instead) — An honest conversation about the difficulty of evaluating agentic systems, log-based eval proxies, and why Sazabi still doesn't buy third-party eval tooling.
    ⚡ MCP Servers, Skills Bloat & Context Management — The tradeoffs between MCP servers and local skill files, progressive tool disclosure, and why context window management is the hidden bottleneck in production agent systems.
    🎯 Building a Moat in 2026 — Sherwood and Demetrios debate what a defensible advantage actually looks like when every AI tool can be cloned fast. Spoiler: "We built it first" is not a moat.
    🚀 Beta Launch & Who It's For — Sazabi is in closed beta and opening the waitlist. If your team uses Cursor or Claude Code and you have production traffic you can't afford to break, this is built for you.
    👉 Perfect for: AI engineers, SREs, DevOps teams, and founders building production-grade agent systems who are questioning whether their current observability stack is overbuilt.

    🔗 Links & Resources
    🌐 Sazabi: https://sazabi.com
    📄 Sazabi on Y Combinator: https://www.ycombinator.com/companies/sazabi
    💼 Sherwood Callaway on LinkedIn: https://www.linkedin.com/in/sherwood-callaway
    📰 SiliconANGLE coverage: https://siliconangle.com/2026/04/08/startup-sazabi-bets-on-logs-and-ai-agents-to-replace-traditional-observability-stacks/
    💻 MLOps.community: https://mlops.community

    ⏱️ Timestamps
    [00:00] Genetic Agent Evolution
    [00:33] Dethroning Datadog
    [03:13] Sazabi vs Traditional Observability
    [10:47] MCP vs CLI Paradigm
    [15:12] Sandbox Usage for Agents
    [24:28] Genetic Prompt Optimization
    [32:34] Eval and Agent Spawning
    [38:45] RL Environment Tensions
    [45:40] Sazabi is hiring!
    [46:10] Wrap up

    #Observability #AIAgents #DevTools
  • MLOps.community

    AI Is Fast. AI Projects Are Slow. Let's Fix That.

    29/05/2026 | 56 min
    Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE — the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.

    AI Is Fast. AI Projects Are Slow. Let's Fix That. // MLOps Podcast #378 with JRocketRide's Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect)A huge shout-out to  ⁨RocketRide⁩  for this collaboration!

    🔑 What's covered:
    🏗️ Why AI infrastructure needs standardization — how coding agents produce inconsistent "glue code" across projects and why a typed node graph fixes it
    ⚡ Efficiency AI vs. Opportunity AI — the two paths companies take with generative AI, and which one actually compounds growth
    🔀 Multi-agent pipeline orchestration — running CrewAI, LangChain, and DeepAgent side-by-side to benchmark which works best for your use case
    💰 Cutting LLM costs in half — design-time strategies for routing tasks to cheaper models without sacrificing output quality
    🔍 Pipeline observability & debugging — logging every node step in dev and production so you can pinpoint exactly where a 10-step pipeline breaks
    🖼️ Beyond text: image, video & audio nodes — frame grabbing, OCR, Whisper transcription, and speech-to-text running on shared GPU infrastructure
    🚀 RocketRide Cloud — one-click deploy from local to cloud with dynamic GPU scaling and cost-efficient shared inference
    🧠 Intentionality in agentic development — why moving fast with AI agents creates "crappy code fast" and how skills/context files change the equation
    🔌 MCP support & framework-agnostic design — swap any model, tool, or framework without rewritesThis episode is essential for AI engineers, ML practitioners, and developers building production LLM applications who want to stop reinventing infrastructure and start shipping.
    🔗 Links & Resources:
    • RocketRide website: https://rocketride.ai
    • RocketRide open source (GitHub): https://github.com/rocketride-org/rocketride-server
    • AIDE VS Code Extension: https://rocketride.org
    • MLOps Community: https://mlops.community
    • Discord: https://discord.gg/Hd4PukFt2H

    ⏱️ Timestamps
    [00:00] Cost Savings in AI
    [00:21] AI, Developer, and Software Development Evolution
    [02:51] Intentionality in Software Development
    [10:51] Model Skill Optimization
    [17:08] Primitives in AI Systems
    [29:00] Coding Agent Challenges
    [37:09] RocketRide Inspiration
    [44:42] Coding Agents and Documentation
    [47:40] RocketRide Cloud Overview
    [56:27] Wrap up
  • 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
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