MLOps.community

Demetrios

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

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

    -7 Ч

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

    Guthrie Cooper leads product for the AI Innovation & Incubation domain at Just Eat Takeaway, the company that launched Europe's first online food ordering platform back in 2000 and is now part of Prosus. He joins an engineering lead from the same team to break down how they run an "incubator within an incubator," shipping prototypes in 2 to 4 weeks and betting on where agentic commerce, voice, and physical AI are heading next. We get into voice agents built on the OpenAI Realtime API, the "beyond the app" strategy of meeting customers inside ChatGPT, Claude, and Gemini, and what it actually takes to ship AI features inside a 25-year-old, 16-country company. What we cover: - Incubator within an incubator: How a pre-seed team turns brand-new protocols into working prototypes in 2 to 4 weeks. - Beyond the app: Why Just Eat is building for ChatGPT, Gemini, and search instead of only defending its own app. - Voice as a mode, not a feature: Interruptible, contextual voice agents, and why a "voice companion" is harder than a voice mode. - Conversational continuity: Keeping context alive across car, phone, and laptop without breaking the flow. - Agentic commerce and MCP: System-to-system and agent-to-agent integrations, plus the privacy and consent line they refuse to cross. - Convenience vs grievance: Why proactive notifications are a churn risk, and how to nudge without getting muted. - Context engineering for voice: Preloading context, parallel tool calls, caching menus, and keeping prompts light so the agent does not hallucinate. - How they decide what to build: Lean startup stage gates, scanning the horizon, painted doors, and killing your darlings. - Ideas that died: Why location-based collection notifications never shipped, and what conversational search accidentally fixed. - Physical AI: Drone delivery with Manna in Ireland, RIVR robot dogs in Switzerland, and orchestrated last-mile handoffs between humans and robots. - AI-first org: AI champions, PMs shipping code, evals, and scaling usability testing with synthetic personas. If you build AI products, work in commerce or logistics, or care about how voice and agents reshape how people buy, this one is for you. Links & Resources: - Just Eat Takeaway: https://www.justeattakeaway.com - Just Eat AI Voice Assistant announcement: https://newsroom.justeattakeaway.com/en-WW/259937-introducing-the-next-evolution-of-ordering-just-eat-takeaway-com-unveils-ai-voice-assistant/ - Manna drone delivery: https://www.manna.aero - OpenAI Agentic Commerce Protocol: https://developers.openai.com/commerce - Model Context Protocol (MCP): https://modelcontextprotocol.io Timestamps 00:00 Meet the AI Innovation & Incubation team 00:30 The incubator within an incubator 02:30 Just Eat's history and the Prosus era 05:00 Beyond the app: ChatGPT vs Google search 07:15 Conversational continuity across devices 09:35 Sharing data with ChatGPT and Claude, and the privacy line 11:18 Health data, Whoop, and the proactive coffee order 13:10 Friction points are why the incubator exists 14:15 Agentic commerce, MCP, and agent-to-agent 20:30 Hyper-personalization and the personal shopper 21:50 Convenience vs grievance: the notification trap 33:50 Super apps, WeChat, and autonomous agents 37:00 How they decide what to build: lean stage gates 48:45 The idea that died: location-based notifications 54:35 Putting ideas on ice: the Mercedes-Benz in-car app 58:30 The hard parts of building a voice agent 1:00:25 Latency, the OpenAI Realtime API, and preloading context 1:08:00 Physical AI: drones in Ireland and robot dogs in Switzerland 1:15:35 AI-first: champions, evals, and synthetic user testing #AgenticCommerce #VoiceAI #DroneDelivery

    1 ч. 19 мин.
  2. Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality

    -6 ДН.

    Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality

    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!

    42 мин.
  3. Agents are Just While Loops

    15 МАЯ

    Agents are Just While Loops

    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

    41 мин.
  4. The Latency Goldilocks Zone Explained

    12 МАЯ

    The Latency Goldilocks Zone Explained

    Rafael (Head of Innovation, iFood) and Daniel (Data and AI Manager, iFood) pull back the curtain on ILO-Agent — iFood's conversational AI ordering system built for 200 million users across Latin America. Recorded live at AI House Amsterdam, this conversation goes deep into the engineering and product decisions behind building recommendation systems and agentic AI, and why the speed of your AI's response might actually be destroying user trust. The Latency Goldilocks Zone Explained // MLOps Podcast #376 with iFood's Rafael Borger (Head of Innovation) and Daniel Wolbert (Data and AI Manager) 🍕 Recommendation Systems at Scale — Why personalizing for 200M users with wildly different food tastes, budgets, and cultures is a fundamentally different problem than standard ML 🤖 ILO-Agent Deep Dive — What iFood's conversational AI agent actually does, how it handles open-ended requests ("a romantic dinner for two, my wife hates onions"), and where it's headed ⏱️ The Latency Goldilocks Zone — The fascinating insight that LLM responses can be too fast (users don't trust them) or too slow (users abandon) — and how to find the sweet spot 🧠 Perceived vs. Actual Latency — Why showing progress indicators and partial results can make a 6-second response feel instant, and how iFood uses this in production 🛒 The Tinder for Food Experience — How iFood is experimenting with swipe-based discovery to solve "I don't know what I want to eat" for millions of undecided users 🗣️ Voice vs. Text AI Interfaces — Why voice ordering limits you to 6 items in 30 seconds, and why text-based agents need radically different output design 🔗 Agent-to-Agent (A2A) Architectures — What happens when your customer support agent and your ordering agent need to collaborate, and the standardization challenges ahead 📊 Measuring Product-Market Fit for AI — Why the Sean Ellis / Chanel score method breaks down in Brazil, and what iFood uses instead 🏗️ Scalability vs. Ecosystem Health — The real tension between consuming partner APIs aggressively and keeping the food delivery ecosystem sustainable 🌎 Building AI for Global-Local Markets — Why one-size-fits-all AI products fail and how iFood builds for cultural and economic diversity simultaneously. This episode is for ML engineers, AI product managers, and data scientists building production AI systems at scale — especially if you're working on recommendation, retrieval, or agentic systems in consumer apps. 🔗 Links & Resources MLOps.community: https://mlops.community AI House Amsterdam: https://aihouse.amsterdam iFood: https://www.ifood.com.br/ iFood AILO launch coverage: https://tiinside.com.br/en/10/10/2025/ifood-lanca-ailo-assistente-de-ia-que-inaugura-pedidos-por-conversa/ iFood AI case study (AWS): https://aws.amazon.com/solutions/case-studies/ifood-bedrock/ Related MLOps Community talk — "From Zero to AILO" by Nishikant Dhanuka & Chiara Caratelli: https://home.mlops.community/public/videos/from-zero-to-ailo-lessons-learned-from-building-ifoods-ai-agent-nishikant-dhanuka-and-chiara-caratelli-2025-11-25 ZenML LLMOps database write-up on iFood's hyper-personalized agent: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agent-for-e-commerce-at-scale ⏱️ Timestamps [00:00] Recommending the unknown [00:18] Ailo Hyperpersonalization Insight [06:24] Predictive Personalization Insights [09:13] "Jet skis" of innovation [17:45] Consumer Behavior and Chatbots [26:33] Perceived Latency and Engagement [33:22] AI-driven UI Evolution [38:17] LCM Voice Mode Inquiry [45:20] Chat as Interface [47:46] Wrap up

    48 мин.
  5. Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

    8 МАЯ

    Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

    Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system. Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over. Building MCP Before MCP Existed: Inside Despegar's Sofia Agent // MLOps Podcast #375 with Nicolas Alejandro Bogliolo, AI PM at Despegar What we cover: - Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow - Decentralized agent ownership: how any squad in the company can build a flow with central supervision - Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise - The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip - KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now - The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task - Group trip planning in WhatsApp groups: the next frontier for travel agents - Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip - Why Despegar held off on giving Sofia the ability to bargain with customers, for now. Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting. Links and Resources: Despegar: https://www.despegar.com Sofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-Assistant Sofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactions MLOps Community: https://mlops.community Subscribe for more agent and AI infra deep dives Timestamps [00:00] Sophia Travel Concierge AI [00:38] Sophia Multi-Agent System [06:00] AI Limitations in Practice [13:52] Travel Planning Exploration [18:03] Group Travel Decision Making [21:32] Agent Ecosystem Design [30:14] Sofia's Travel Assistant Vision [33:35] Orchestration and MCP Design [40:13] Sophia Negotiation Concerns [40:47] Wrap up #AIAgents #MCP #AgenticAI

    41 мин.
  6. Voice Agent Use Cases

    1 МАЯ

    Voice Agent Use Cases

    This episode is brought to you by the MLflow team. Check out more information at MLflow.org. What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI engineer who built customer support systems handling 2 billion+ interactions — now working on next-gen voice agent platforms. Anurag digs deep into the real engineering tradeoffs, design patterns, and use cases that separate production-grade voice agents from demos. Voice Agent Use Cases // MLOps Podcast #374 with Anurag Beniwal, Member of the Technical Staff at ElevenLabs 🎙️ Topics covered: 🔹 Cascaded vs. speech-to-speech — Why cascaded systems still win in production, and how to make them feel natural without sacrificing control 🔹 Latency masking — Foreground/background model architecture and how to buy yourself time while deep retrieval runs 🔹 Constellation of models — Using Haiku for tool calling, fine-tuned smaller models for response generation, and why "one model for everything" breaks at scale 🔹 Turn-taking & ASR challenges — Why voice is harder than chat: accents, noise, silence detection, and domain-specific fine-tuning 🔹 Level 1 vs Level 2 customer support — Why today's agents max out at Level 1 and what it takes to capture Level 2 expert judgment 🔹 Inbound vs. outbound sales agents — Where voice agents are already winning, and why inbound lead qualification beats cold outbound 🔹 Booking, reservations & concierge — The clearest near-term wins for voice agents across hospitality, home services, and SMBs 🔹 Continual learning from natural language feedback — How to build agents that improve from real operator feedback without ML expertise 🔹 Conversational TTS — Why passing full conversation history to your TTS model changes everything for tone consistency 🔹 User tiers for voice platforms — Non-technical business owners vs. developers vs. enterprise: why one interface doesn't fit all. If you're building production voice agents, evaluating voice AI vendors, or scaling AI-first customer support — this episode is packed with hard-won lessons from someone who's done it at Amazon scale. 🔗 Links & Resources: MLOps.community: https://mlops.communityGoogle Scholar: https://scholar.google.com/citations?user=g_QB5WgAAAAJ&hl=en&o Amazon science page: https://www.amazon.science/author/anurag-beniwal Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide ⏱️ Timestamps [00:00] Cascaded Systems Control Challenge [05:35] Voice vs Chat Complexity [14:16] MLflow's open source platform [15:03] AI Model Constellations [23:00] Model Constellations Use Cases [31:40] Voice vs Text Context [33:54] Voice as Thought Capture [42:11] Cascaded vs Speech-to-Speech Debate [50:02] Wrap up

    51 мин.
  7. The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding

    24 АПР.

    The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding

    Jesse Vincent is the Founder & CEO of Prime Radiant and creator of Superpowers — the most-used Claude Code plugin in the world. He built the first agentic software development methodology from scratch while managing MIT interns in the early 2000s, and hasn't written a line of code manually since October. The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding // MLOps Podcast #373 with Jesse Vincent, Founder & CEO of Prime Radiant In this conversation, Jesse walks Demetrios through the full Superpowers system: why he thinks most developers are still approaching agentic coding wrong, how he designs skills that force LLMs to stop rationalizing and actually follow rules, and what he's building next at Prime Radiant — including Green Field, an unreleased tool for reverse-engineering legacy codebases into specs. This one is for developers who want to go beyond "vibe coding" and build AI-assisted workflows that actually scale. 🔧 Topics Covered 🧠 The Superpowers Methodology — How the brainstorming skill extracts what you actually want before you hand work to an agent, and why most developers skip this step 📋 Spec-Driven Development & Plan Files — Why Jesse insists on TDD, DRY, and YAGNI for every agentic task, and how planning skills generate per-task context blocks agents can actually execute on 🐛 Debugging with Agents — Jesse's systematic approach to root cause analysis, reproduction cases, and the 30 years of debugging instinct he's baked into a skill 🔄 Pressure Testing LLM Skills — How Claude fires up sub-agents and stress-tests its own rules to catch rationalization before it shows up in production 🛠️ Clearance IDE — Jesse's new Markdown-native development environment built for humans working alongside AI, with a history pane for file navigation 📦 Green Field (Unreleased) — A toolset for turning old codebases or built products into clean specs — not yet public but dropping soon from Prime Radiant 🧑‍💼 Management as the Magic Trick — Why the real unlock of tools like Superpowers is that they make every developer a manager, and why that transition is hard the first time ⚖️ Software Ethics in the Agent Era — Reverse engineering, license washing, open source cloning, and whether the value of software itself is collapsing 🔗 Links & Resources Prime Radiant: [https://prime-radiant.com](https://prime-radiant.com/) Superpowers on GitHub: https://github.com/prime-radiant-inc Clearance IDE: https://github.com/prime-radiant-inc (check repo) MLOps.community Slack: https://go.mlops.community/slack MLOps.community website: [https://mlops.community](https://mlops.community/) ⏱️ Timestamps [00:00] Greenfield Toolset Insights [00:27] Superpowers Kit Evangelism [08:06] Hyperbolic's GPU Cloud [17:48] Debugging Skill Creation [22:12] Skill Extraction Strategy [31:15] Smallest Harness [41:06] Software supply chains [48:56] Visual Precision Challenges [54:09] Creative Feedback Loops [1:04:24] MLflow's Gen AI [1:05:55] Wrap-up

    1 ч. 7 мин.
  8. It's 2026, and We're Still Talking Evals

    21 АПР.

    It's 2026, and We're Still Talking Evals

    Maggie Konstanty is an AI Product Manager at Prosus, one of the world's largest consumer internet companies, where she builds and evaluates AI agents for food ordering and ecommerce at scale. She's been inside the messy reality of LLM evaluation longer than most — and her take is unfiltered. It's 2026, and We're Still Talking Evals // MLOps Podcast #372 with Maggie Konstanty, AI Product Manager at Prosus 🧪 Why accuracy metrics lie — Maggie breaks down why "95% accurate" tells you almost nothing about whether your agent is actually working in the real world, and what to measure instead. 🏗️ Pre-ship vs. production evals — Your eval suite before launch will not survive first contact with real users. Maggie explains the structural disconnect and how to close the gap. 👻 The silent failure: user drop-off — Users who are unhappy don't complain — they just leave. Discover why drop-off analytics are one of the most underutilized eval signals in production. 🎯 Instruction to fail: the 20-evaluator trap — Setting up 20 types of evaluators not connected to your product goal is a fast path to wasted time. How to design evals that are tied to real outcomes. 🍽️ The "surprise me" edge case — A real example from Prosus's food ordering agent and what it reveals about how users actually behave vs. how PMs imagine they do. 🤖 LLM-as-a-judge: the limits — Why Maggie doesn't lean on LLM-as-a-judge for accuracy measurement, and what approaches she uses instead for production-grade evaluation. 🛠️ Arize/Phoenix & eval tooling critique — A candid take on the current state of eval platforms, why she spent a whole day fighting the UI, and why mature teams often go back to custom code. 🧬 Eval as team DNA — Evals aren't a launch checklist. Maggie makes the case that they need to be a constant practice embedded in team culture — and why alignment on "what good looks like" is harder than any technical implementation. 🔢 When to stop optimizing — What happens when your eval score approaches 100%, and how to know when it's time to shift focus to a different metric or flow. 💬 Red teaming with incentives — A fun tactic: running adversarial eval sessions where engineers compete to break your agent for an Amazon gift card. This is required watching for AI PMs, ML engineers, and applied AI teams who have moved past "getting evals set up" and are now struggling with making them actually matter.--- 🔗 Links & Resources Maggie Konstanty on LinkedIn: https://www.linkedin.com/in/maggie-konstanty Prosus: [https://www.prosus.com](https://www.prosus.com/) MLOps.community: [https://mlops.community](https://mlops.community/) Arize AI / Phoenix (mentioned): [https://arize.com](https://arize.com/) / [https://phoenix.arize.com](https://phoenix.arize.com/) MLOps.community Slack: https://go.mlops.community/slack ⏱️ Timestamps [00:00] Evaluations and User Alignment [00:18] Eval Lifecycle in Production [06:05] LLM Accuracy and Judging [15:30] Evals vs Tests in AI [22:39] Profanity as Frustration Signal [29:23] Impact-weighted performance [32:22] Eval Tooling Pros and Cons [38:10] Build vs Buy Dilemma [39:35] Wrap up

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

Информация

  • Автор
    Demetrios
  • Годы выхода
    2020 - 2026
  • Выпуски
    520
  • Ограничения
    С ненормативной лексикой
  • Авторские права
    © Demetrios
  • Сайт подкаста
    MLOps.community

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