PerceptiLabs Blog

MLOps: An “Ops” Just for Machine Learning

Feb 10, 2020 8:01:00 PM / by PerceptiLabs posted in Model Management, Machine Learning Workflow, MLOps, Explainability

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If you’ve been around software development for a while, you’ve undoubtedly come across numerous terms appended with the word “Ops”, such as “DevOps”, “TestOps”, or “DataOps”. Of these, “DevOps” (short for “development and information-technology operations”) is probably the most well-known. It refers to a set of software development practices that promote automation and cross collaboration between teams of different disciplines, to reduce software delivery times while achieving a desired level of quality. 

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The Importance of Transparency in Machine Learning Models

Jan 29, 2020 7:59:00 PM / by PerceptiLabs posted in Transparency, Machine Learning, Explainability, Modeling Tool

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Digital systems are only useful if they can be trusted to do their job. With traditional deterministic systems, we could derive a degree of certainty that a system was working correctly through processes such as unit tests, end-user testing, code reviews, and design documentation.

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