Explainability - PerceptiLabs

An Overview of TensorFlow and how PerceptiLabs Makes it Easier

In A New Visual Approach to Machine Learning Modeling, we talked about how TensorFlow is one of the most popular machine learning (ML) framework today, but it’s not necessarily

Transparency

A New Visual Approach to Machine Learning Modeling

Machine learning (ML) tools are exploding and specializing, giving users the option to build and manage their ML models in different ways ranging from writing code, relying on frameworks to

Machine Learning

Venture Beat: Getting inside the head of a machine learning scientist

We were excited to be featured in Venture Beat, in their Article titled: Getting inside the head of a machine learning scientist. Check it out to see how PerceptiLabs allows

Machine Learning

Four Common Types of Neural Network Layers (and When to use Them)

Neural networks (NN) are the backbone of many of today's machine learning (ML) models, loosely mimicking the neurons of the human brain to recognize patterns from input data. As a

Model Management

MLOps: An “Ops” Just for Machine Learning

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

Transparency

The Importance of Transparency in Machine Learning Models

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