Earlier this week IBM announced the release of IBM AutoAI.
IBM AutoAI is a new suite of capabilities for IBM Watson Studio that aims to streamline data prep and preprocessing to help teams through common tasks that can frustrate and delay nascent AI projects. The tools enable organizations quickly build ML models and experiments, so teams can focus their efforts on activities like designing, testing and deploying ML models.
IBM says AutoAI is designed to let users quickly scale ML experimentation and deployment processes, and incorporates a powerful suite of model types, such as gradient boosted trees, for enterprise data science. Also included in the tooling is IBM Neural Networks Synthesis (NeuNetS), which enables development of deep-learning models. Currently in open beta within Watson Studio projects, NeuNetS synthesizes custom neural networks that can be optimized for speed or accuracy.
According to IBM, the capabilities of AutoAI can bring success to AI projects that are otherwise at risk of failure. The company cites a recent Forrester report (Forrester Infographic: AI Experiences A Reality Check), which warns: “Most enterprise AI models don’t make it into production, and many stall at the pilot or proof-of-concept phase, even when they show value.”
The Forrester report survey found that 60 percent of respondents cited managing data quality as a top challenge with AI deployments, while 44 percent singled out data prep as a key challenge.
These findings are little surprise to Rob Thomas, General Manager for IBM Data and AI:”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,” he says. “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.”