International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

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The post IBM Watson Studio’s Latest AutoAI Tool to Automate Processes appeared first on Artificial Intelligence.

Deploying AI-imbued apps and services isn’t as challenging as it used to be, thanks to offerings like IBM’s Watson Studio (previously Data Science Experience). Watson Studio, which debuted in 2017 after a 12-month beta period, provides an environment and tools that help to analyze, visualize, cleanse, and shape data; to ingest streaming data; and to train and optimize machine learning models in real time. And today, it’s becoming even more capable with the launch of AutoAI, a set of features designed to automate tasks associated with orchestrating AI in enterprise environments.

“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 general manager of IBM Data and AI Rob Thomas in a statement. “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 machine learning models and experiments faster.”

AutoAI, which is also available in IBM Cloud, automates data prep and preprocessing steps including feature engineering, or the process of using domain knowledge of data to create elements core to AI algorithms. It handles hyperparameter optimization (i.e., choosing a set of optimal hyperparameters for a learning algorithm, where “hyperparameter” refers to the value set before the learning process begins), and it boasts a growing suite of powerful pretrained model types such as gradient boosted trees.

Also on tap in Watson Studio AutoAI is IBM’s Neural Networks Synthesis, or NeuNetS, a platform intended to expedite deep learning model development by leveraging AI to synthesize custom neural networks automatically, and by allowing users to choose to prioritize speed or accuracy. It runs on IBM Cloud with Kubernetes, and it doesn’t require knowledge of code or experience with deep learning frameworks, IBM says.

NeuNetS launched in preview last fall and is available in beta within Watson Studio projects.

Watson Studio AutoAI’s unveiling comes months after IBM brought Watson Studio, Watson Assistant, and AI OpenScale to private clouds and public platforms like Google Cloud Platform, AWS, and Microsoft’s Azure through an integration with IBM Cloud Private for Data. Coinciding with that news, IBM launched AI Digital Automation, a service that collects and analyzes patterns in data to identify tasks that can be automated.

IBM competes with Google, Microsoft, Amazon, and others in the machine learning as a service (MLaaS) market, which is anticipated to reach $5.5 billion by 2023. Microsoft in early March announced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps. In April during its I/O 2019 developer conference, Google introduced AutoML Video and AutoML Tables for structured data, two new classes for Google’s suite of services that automate the creation of AI systems. And just this week, Amazon announced the general availability of Amazon Personalize, an AWS service that facilitates the development of websites, mobile apps, and content management and email marketing systems that suggest products, provide tailored search results, and customize funnels on the fly.

The post IBM’s Watson Studio AutoAI automates enterprise AI model development appeared first on Artificial Intelligence.