Category: Software, Data, Microsoft, Infrastructure, artificial-intelligence

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As a pager-carrying firefighter for Amazon’s machine learning systems, found that the DevOps tools built around debugging applications fell far short of the needs of operators in production. “What I discovered is that the tools that I am used to, supporting traditional software applications and on the retail website, do not map well to the needs that I have when I’m supporting an [artificial intelligence] application,” she said. However, most observability tools lack an essential element for machine learning practitioners. “[It] helps the debugging of machine learning models take into account how ever-changing real-world data affects the behavior of these machine learning-powered applications.” The platform, built atop whylogs, provides monitoring and observability of ML applications through a purpose-built user interface that collects information about all your models in one place.

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