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    Towards stability and robustness

    Towards stability and robustness

    FromPractical AI: Machine Learning, Data Science

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    Towards stability and robustness

    FromPractical AI: Machine Learning, Data Science

    ratings:
    Length:
    49 minutes
    Released:
    Jul 20, 2021
    Format:
    Podcast episode

    Description

    9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems.
    Released:
    Jul 20, 2021
    Format:
    Podcast episode

    Titles in the series (100)

    Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, and more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!