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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For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.

Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.

“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,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”

In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).

IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.

Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.

Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.

For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.

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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.

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Microsoft Corp has what may sound like a counter-intuitive request: Please try to hack into Azure more often.

The company isn’t encouraging malicious attacks but it does want security researchers to spend more time poking holes in its flagship cloud service so the company can learn about flaws and fix them.

Many so-called White Hat hackers do this for the company’s older products like Windows, Office and browsers, but there aren’t enough working on Azure, said Kymberlee Price, who oversees community programmes in Microsoft’s Security Response Center. The company is planning several steps to change that, including explicitly stating it won’t take legal action against researchers and creating a game-like reward system that gives successful bug-finders perks and bragging rights.

Microsoft currently offers bug bounty payments for Azure, but “it’s just not getting as much activity as I would like to see”, Price added.

It’s an issue Microsoft needs to worry about as it bets big on cloud services for revenue growth. The shift to cloud computing is changing cybersecurity, providing new opportunities and new challenges. One of the biggest risks is that Microsoft now runs services for customers in its cloud, which means the software giant is on the hook to protect them.

Microsoft is planning to release what’s called a Safe Harbour statement giving researchers legal clearance to report a vulnerability. “We’ve always done that but we’ve never formally articulated it,” Price said. It’s important to publish a formal policy as researchers work more on cloud systems where they may worry they’ll accidentally knock a service offline or access customer data and get in trouble, she said.

In her first stint at Microsoft in the 2000s, Price was one of the security engineers pioneering the company’s effort to collaborate with security researchers, rather than viewing them as adversaries. She left in 2009 and returned a little more than two years ago.

Right now attackers still target networks located at a company’s own offices more frequently than the cloud, but that’s changing, said Azure chief technology officer Mark Russinovich. “The level of sophistication of the attackers and the interest in (attacking) the cloud just continues to grow as the cloud continues to grow,” he added.

Cloud security requires new tools and techniques but it also enables companies like Microsoft to track and analyse vast amounts of data to find malicious actors and scan networks of hundreds of thousands of customers so it can see attacks materialise.

Russinovich spoke about protecting the cloud at an academic conference at Microsoft attended by hundreds of Microsoft workers and security engineers from Amazon Web Services, Google, Nike Inc. and others. The event grew out of a trail-running group that includes Microsoft’s Ram Shankar Siva Kumar, who oversees a team of engineers who apply machine-learning to cybersecurity, and peers at AWS and Google. The group would often share techniques and research while on the trail and the idea for a formal conference to exchange ideas was born.

The hope is that sharing data, tools and techniques publicly will help everyone better fend off attackers. As long as private customer information is protected, Microsoft wants to share security data, said Steve Dispensa, general manager, cloud and AI security at Microsoft.

“The idea that we’re smarter than the attackers is a malignant myth – they know before we do where the weak spot is,” he said. “We publish data, we all learn, a rising tide lifts all boats.” – Bloomberg

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CognitiveScale Inc., an Enterprise AI software pioneer, has expanded its strategic relationship with Microsoft to further accelerate adoption of CognitiveScale’s Cortex software amongst businesses using Microsoft Azure.

Today, enterprises spend weeks deploying AI platforms and building intelligent business processes in their cloud or data center environments. As a result, AI projects take a long time to get started and deliver value. Built on open and extensible specifications, Cortex simplifies the design, development, delivery and management of cognitive business processes that get smarter and better with time. In recent tests, Cortex successfully reduced AI build time by 65% and overall AI DevOps time by 94% through its unique virtualization technology.

With CognitiveScale Cortex now deeply integrated with Microsoft Azure and available on the Microsoft Azure Marketplace, businesses will be able to quickly deploy the full Cortex product suite in their Azure cloud environment. Cortex software also now integrates seamlessly with a suite of Azure models ranging from Natural Language Processing (NLP) to Computer Vision that can be used by enterprises to speed up their AI system development.

Having worked together since April 2017 to bring industry specific cognitive systems jointly to the market, the collaboration will enable businesses to quickly and easily deploy the Cortex platform and agents, reducing time and cost of deployment, which are major considerations when adopting AI platforms. The expansion also reaffirms Azure as CognitiveScale’s preferred cloud provider.

“Deepening our relationship with Microsoft Azure helps put Cortex at the fingertips of millions of businesses across the globe while giving them the freedom to rapidly develop and deploy trusted AI solutions to solve their unique business challenges,” said Akshay Sabhikhi, CEO of CognitiveScale. “Enterprises on Azure can now seamlessly apply our powerful AI software to transform customer engagement as well as boost employee expertise and operational productivity within a matter of minutes, while keeping control of their most sensitive data within their virtual enterprise.”

Ed Fandrey, U.S. Vice President of Financial Services at Microsoft said, “We’re pleased to enable close collaboration between Microsoft and CognitiveScale so customers can more rapidly develop and deploy AI-powered applications with the power of Microsoft Azure, for use cases across financial services, healthcare, digital commerce and other industries.”

Chris Corrado, COO and CIO of London Stock Exchange Group said, “The most successful companies today are relentlessly focused on bringing agility and scale to digital services and applications. We are excited about working with both Microsoft and CognitiveScale to realize our strategy of delivering breakthrough AI innovations that bring efficiency, transformation, and transparency to the financial markets.”

At the recent London Tech Week, Akshaya Bhargava, ex-CEO Barclays’ Wealth and Founder and CEO of Bridgeweave, along with CognitiveScale and Microsoft, talked about how AI is transforming the financial services sector. Bridgeweave’s Investment Insightsis a new fintech product that is targeted toward self-directed investors. It has the potential to democratize investing by providing institutional asset management level AI-powered trading insights to individual investors who want to participate in the stock market. The product has been built using CognitiveScale’s Cortex software and runs on Microsoft’s Azure.

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