New AutoAI capabilities designed to help advance AI development by automating and speeding time-intensive data processes, while freeing-up data scientists to focus on machine learning
IBM (NYSE: 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 – the work of AI.
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. In a recent Forrester1 report 60% of respondents said managing data quality was among the top challenges faced when trying to deliver AI, while another while 44% attributed it to data prep. For organizations with no data scientists, AI projects are challenged even more. In an IBM Institute for Business Value study, Shifting Toward Enterprise-Grade AI,2 last year 63% of respondents said a lack of proper technical skills was a prime challenge to AI implementations.
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.
Available now in Watson Studio on the IBM Cloud, the new AutoAI 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.
“IBM has been working closely with clients as they chart their paths to AI, and one of the first challenges many face is data prep – a foundational step in AI,” said Rob Thomas, General Manager, IBM Data and AI. “We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources. The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”
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.
The Watson Studio AutoAI work, which leverages key technologies developed in IBM Research, builds on automation capabilities IBM has been developing and offering across its portfolio for years. Solutions ranging from IBM Watson Assistant and Discovery, to Watson Machine Learning, offer varying degrees of automation that speeds and simplifies time-consuming tasks enabling clients to focus on higher-value work faster.
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Learn more about AutoAI here: https://www.ibm.com/cloud/watson-studio/autoai
1 Forrester Research, Infographic: AI Experiences A Reality Check, May 2019
2 IBM Institute for Business Value: Shifting Toward Enterprise-Grade AI, Sept. 2018
SOURCE IBM
