Over the last several years, machine learning (ML) has transitioned from a discipline once reserved for researchers and PhDs into a lucrative field comprised of a growing and diversifying set of users and ML practitioners. This is due in part to the increased processing power found in today's hardware, the discovery of new ML algorithms, and the growing number of open source ML tools, frameworks, and datasets. Collectively, these factors are democratizing ML by putting new and more powerful ML capabilities into the hands of more users and ML practitioners than ever before. However, despite all of these advances, many ML tools and frameworks fail to address the overall workflow of designing, training, and tuning ML models. Many frameworks such as TensorFlow and PyTorch, while incredibly powerful, are still fundamentally programmatic frameworks aimed at coders.

TensorFlow is arguably the most popular machine learning (ML) framework today because of its rich multi-layer API. However, as a framework for ML modeling via code, TensorFlow can be a handful for beginners. Even experienced data scientists and developers can find it difficult when working with large sets of code to visualize the model, to see how changes to logic and hyperparameters affect the model, and to track down bugs. Just released PerceptiLabs 0.11, is quickly becoming the GUI and visual API for TensorFlow that aims to solve these challenges. It's built around a sophisticated visual ML modeling editor in which you drag and drop components and connect them together to form your model.