IBM Adds Automation Capabilities to Watson Studio

IBM announced AutoAI, a new set of capabilities for Watson Studio designed to automate many of the often complicated and laborious tasks associated with designing, optimizing and governing AI in the enterprise. As a result, data scientists can be freed up to dedicate more time to designing, testing and deploying machine learning (ML) models.

Despite a growing awareness of the strategic value of AI in business, most organizations still grapple with fundamental information architecture challenges. The chores of finding, collecting and organizing fragmented and siloed data, and then preparing that data for analysis and ML comprises is often slowing AI development.

Watson Studio's new AutoAI capabilities work in conjunction with Watson Machine Learning to begin to remedy these challenges by automating and speeding a variety of the steps in the AI lifecycle. New capabilities are designed to automate the time-consuming processes of data prep and preprocessing, including model development and feature engineering.

This is designed to enable users to leverage hyperparameter optimization capabilities to build data science and AI models with greater ease. In addition, AutoAI contains a suite of the most powerful model types for enterprise data science, such as gradient boosted trees, and is engineered to let users quickly scale ML experimentations and deployment processes.

Also included in the AutoAI family is IBM Neural Networks Synthesis (NeuNetS), first previewed last fall and currently in open beta within Watson Studio projects. The technology is designed to fast-track the development of deep-learning models by using AI to automatically synthesize customized neural networks. NeuNetS enables users to choose whether to optimize for speed or accuracy, and watch the model build and train itself in real-time.