• ML Platform Podcast

  • 著者： neptune.ai
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ML Platform Podcast

著者： neptune.ai
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  • Get behind-the-scenes insights into the world of internal ML platforms and MLOps stack components with Piotr Niedźwiedź and Aurimas Griciūnas in their show, where together with ML platform professionals, they discuss design choices, best practices, and real-world solutions to MLOps challenges. Brought to you by neptune.ai.
    neptune.ai
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あらすじ・解説

Get behind-the-scenes insights into the world of internal ML platforms and MLOps stack components with Piotr Niedźwiedź and Aurimas Griciūnas in their show, where together with ML platform professionals, they discuss design choices, best practices, and real-world solutions to MLOps challenges. Brought to you by neptune.ai.
neptune.ai
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エピソード
  • Navigating Vector Databases: Indexing Strategies, GPU, and More

    Navigating Vector Databases: Indexing Strategies, GPU, and More
    2024/06/21
    On this episode of the ML Platform Podcast, Frank Liu discusses the basics, problems and challenges of vector databases, including indexing strategies, segmentation, vector lengths used in production, GPU-accelerated vector databases, potential use cases, and more.
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    1 時間 11 分
  • Learnings From Building the ML Platform at DoorDash With Hien Luu

    Learnings From Building the ML Platform at DoorDash With Hien Luu
    2024/06/07
    On this episode of the ML Platform Podcast, Hien Luu talks about building the ML Platform at DoorDash, including big data models, building platforms at the enterprise level, centralized vs. decentralized ML platform teams, LLM strategy, MLOps in the shadow of LLMs, and more.
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    1 時間 6 分
  • Breaking Down Workflow Orchestration and Pipeline Authoring in MLOps

    Breaking Down Workflow Orchestration and Pipeline Authoring in MLOps
    2024/05/24
    On this episode of the ML Platform Podcast, Adam Probs and Hamza Tahir discuss workflow orchestration and pipeline authoring in MLOps, focusing on ZenML's capabilities. They cover the main jobs-to-be-done, challenges behind testing integrations, ZenML’s role in end-to-end ML platforms, ZenML Cloud, the future of MLOps with LLMs, and more.
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    52 分

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