TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ostensibly will enable analysts to improve ML model accuracy significantly and shorten development cycles. Workbench does this while using familiar tools, workflows, and libraries in a single environment that plugs directly into existing data pipelines and ML infrastructure, TigerGraph VP Victor Lee told VentureBeat. The ML Workbench is a Jupyter-based Python development framework that enables data scientists to build deep-learning AI models using connected data directly from the business. Graph-enabled ML has proven to have more accurate predictive power and take far less run time than the conventional ML approach. Conventional machine learning algorithms are based on the learning of systems by training sets to develop a trained model.

One thing about Microsoft anything -- they have 50 ways to solve the same problem, seemingly! But there are nuances, so you can find the exact/perfect tool for your needs. So the problem is, creating ML experiments & models. And there seem to be two ways of doing this, ML Studio, and ML workbench. Long story short, ML Studio is browser only, zero install.