IBM recently released its AutoAI, which is a new set of capabilities for its Watson Studio. It is designed to automate many of the often complicated and tedious tasks associated with designing, optimizing and governing Artificial Intelligence in the enterprise. Because of this, data scientists can be available to dedicate more time to designing, testing and deploying machine learning (ML) models.
“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, GM, Data and AI, IBM. “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.”
Despite a growing knowledge of the strategic value of AI in business, most organizations still struggle with fundamental information architecture challenges. The responsibilities 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 Forrester report, 60 percent of respondents said managing data quality was among the top challenges faced when trying to deliver AI, while another while 44 percent attributed it to data preparation. For organizations with no data scientists, AI projects are challenged even more. In an IBM Institute for Business Value study entitled, “Shifting Toward Enterprise-Grade AI,” last year 63 percent of respondents said a lack of proper technical skills was a prime challenge to AI implementations.
To aide in this endeavor, 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.
Using Watson Studio on the IBM Cloud, offers new AutoAI capabilities which are designed to automate the time-consuming processes of data prep and preprocessing. These processes include model development and feature engineering. The product is designed to enable users to leverage hyperparameter optimization capabilities to build data science and AI models with greater ease.
What else does it offer? AutoAI contains a suite of the most powerful model types for enterprise data science such as gradient boosted trees. The model types are engineered to let users quickly scale ML experimentations and deployment processes.
The Watson Studio AutoAI leverages key technologies developed in IBM Research. It 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.
Chrissie Cluney has been a correspondent for IoT Evolution World since 2015. She holds a degree in English with a concentration in writing from the College of Saint Elizabeth.
Edited by Ken Briodagh