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.
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.
Introducing PerceptiLabs, a Visual Modeling Tool for Machine Learning
We were excited to be featured in Venture Beat, in their Article titled: PerceptiLabs’ drag-and-drop interface makes ML modeling easier and faster. Check it out to learn a little more about our visual modeling tool and how we got started.
In our blog The Importance of Transparency in Machine Learning Models, we talked about how transparency in machine learning (ML) models helps us to build an understanding of the model, provide insight into why it’s generating certain results, and ultimately increase our trust that the model will perform as expected in the real world. However, achieving such transparency in a typical real-world ML workflow requires the right processes and tools to be in place.