ARMONK, N.Y., June 12, 2019 /PRNewswire/ -- IBM (NYSE:
IBM) today 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.
About IBM Data and AI
For more information go to
https://www.ibm.com/analytics/
Contact
Michael
Zimmerman
IBM Media Relations
mrzimmerman@us.ibm.com
1 Forrester Research, Infographic: AI Experiences
A Reality Check, May
2019
2 IBM Institute for Business
Value: Shifting Toward Enterprise-Grade AI,
Sept. 2018
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SOURCE IBM