The Practical AI Digest

Mo Bhuiyan

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

  1. 6日前

    Distillation: How Small Models Eat Big Models for Lunch

    Training a frontier model costs millions. Distilling a capable student from it costs thousands. Knowledge distillation is quietly reorganizing the economics of the entire AI industry.

  2. 8月13日

    World Models: When AI Learns Physics Instead of Memorizing Data

    A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap between AI that talks about reality and AI that can act in it.

  3. 7月30日

    The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are

    MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your data, for your task.

  4. 7月16日

    Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

    A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.

  5. 7月2日

    The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

    90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.

  6. 6月18日

    KV Cache Compression: The Memory Wall Nobody Talks About

    Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are about to change the math entirely.

  7. 6月4日

    Context Rot: Why Million-Token Windows Quietly Fail

    Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner playbook for what to do Monday.

    Context Rot: Why Million-Token Windows Quietly Fail

  8. 5月26日

    LLMOps: Operating Large Language Models in Production

    Building an AI model is one thing: keeping a large language model running reliably in the real world is another. In this episode, we discuss LLMOps, the emerging set of practices and tools for deploying, monitoring, and maintaining large language models (LLMs) in production. We cover challenges unique to LLMs (like handling the huge model sizes, long context lengths, unpredictable outputs, and continuous updates with new data). You’ll learn about techniques for versioning and evaluating LLMs, setting up feedback loops (human or automated) to catch issues like drift or toxicity, and infrastructure like model hubs and the new Model Context Protocol (MCP) that connects LLMs with external tools and data. We tie it together with examples of how companies manage AI like GPT-4 as a service, ensuring it stays efficient, safe, and up-to-date post-deployment.

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番組について

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

情報

  • クリエイター
    Mo Bhuiyan
  • 配信期間
    25年 - 26年
  • エピソード
    27
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Mo Bhuiyan
  • 番組のWebサイト
    The Practical AI Digest

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