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Cloud AI services slowly but surely lure enterprise IT shops

As IT pros experiment with AI, many will do so on the public cloud. But choosing from an ever-growing list of AI services, from AWS, Azure and others, is no easy task.

Artificial intelligence is no longer reserved for sci-fi flicks, but the technology is still alien to many enterprise IT teams. Nevertheless, one adoption trend is clear: The public cloud will be the go-to destination for most enterprise AI workloads.

"I'm not saying that AI won't happen in the enterprise in people's data centers, but this is a workload that will predominantly happen in the cloud," said Rob Koplowitz, principal analyst at Forrester Research.

Some organizations will choose to keep AI applications -- particularly those that contain sensitive customer data -- in-house, as they do for other workloads with strict security or compliance requirements. But, in general, public cloud AI services will be the predominant model, agreed Adrian Bowles, lead analyst at Aragon Research.

One of the biggest reasons the cloud is a particularly good fit for AI is experimentation, Bowles said. Because most organizations are still exploring potential uses for technologies such as machine learning, predictive analytics or natural language processing, they want an environment that lets them experiment, without significant financial investment or risk.

"A large percentage of the enterprises that are using the public cloud right now for AI are using it as a test bed -- an inexpensive way to get started, and to figure out which applications are going to be amenable to different forms of AI," Bowles said.

Public cloud platforms, including Amazon Web Services (AWS) and Microsoft Azure, allow organizations to test different machine learning algorithms, for example, to see what might be possible with their data. From there, organizations can choose between two options: fail it or scale it.

"If your [AI application is] going to fail, great -- then you can move on to something else," Bowles said. "If it scales, then you're already in a place where you can scale rapidly ... [The cloud] frees you up for that experimentation."

What's more, organizations often choose the public cloud for AI deployments because of the range of resources available.

"[In the cloud], it's easier to say, 'Well, I'm going to start with some natural language processing,'" said Nicola Morini-Bianzino, global lead of the artificial intelligence practice at Accenture, a consulting and professional services firm. "Then, you can move some of your data onto the cloud and decide to do something different around computer vision, and just extend and use those APIs on top of the infrastructure and data you have already created."

Public cloud also eliminates the need for organizations to invest in expensive specialty hardware that many AI workloads require. Most of the major public cloud providers, for example, now offer cloud instances based on GPUs, which are especially beneficial for compute-intensive AI workloads, Koplowitz said.

Challenges with AI in the public cloud

Of course, any emerging technology, including AI, presents the enterprise with a learning curve. IT teams may not need to overhaul their underlying cloud infrastructure for an AI deployment, but they must evolve their skill sets in other ways to adopt a more datacentric mentality, Morini-Bianzino said.

For a successful AI deployment, IT teams must hone their data analysis skills and learn to recognize certain patterns or relationships within large enterprise data sets -- because AI is only valuable as the data you feed into it.

"The value of [a machine learning] algorithm is a direct function of the value of that data that you are pushing through the algorithm," he said. "So if the data is not good, the algorithm is not good either."

Data analysis skills are increasingly important as IT teams pursue machine learning, Bowles agreed. Part of this is because, with machine learning, IT systems can improve their performance over time through exposure to data, rather than through reprogramming.

In addition, infrastructure management teams should attempt to break down barriers with developers. The more admins learn how AI applications are built and consumed, the smarter choices they can make from an infrastructure perspective, said Lori Brown, managing consultant for the healthcare IT group at PA Consulting.

"As [IT teams] see the way AI development changes, and what impact that has on their infrastructure and consumption, they can be wiser about how they make their purchases of public cloud services to support AI," Brown said.

Pick your brain

Another big challenge IT teams face with AI applications is how to choose a cloud provider. As vendors release new cloud AI services at a dizzying pace, it can be tough to know where to start.

The top public cloud providers have emerged as dominant AI vendors as well: AWS, Azure, Google and IBM. Each vendor varies in strengths, weaknesses and use cases, but their individual services cover several common AI features: machine learning, image recognition, natural language processing and text-to-speech capabilities. And niche players in the cloud provider market have yet to mount a challenge.

AWS, the leader in public cloud adoption, pulled back the curtain on three AI-based services at its re:Invent conference in 2016. Amazon Rekognition provides a platform for image processing, Amazon Polly uses deep learning to turn text into speech, and Amazon Lex uses the same automatic speech recognition technology as Alexa so developers can build conversational interfaces with voice and text. The ability to integrate AWS' various compute, storage, content delivery, and developer tools entice enterprises as much as or more than its Amazon AI suite alone.

In addition to its popularity as an app-dev platform, the popular Amazon Echo smart home device gives enterprises an in-road to package applications that interact with consumers.

"In the same way that we used to think about capturing eyeballs, if voice is going to become a prevalent way of interacting with computers, then there's a great deal of value in being the system that captures your words," Koplowitz said. "There were an awful lot of [Amazon Echo devices] sold at Christmas last year, and there's a pretty good chance that I can reach you through that device."

At its Build conference in early May, Microsoft  appealed to enterprise employees with Microsoft Graph, a service that gains insights from employee activity to improve productivity and plan meeting times and collaborators for projects. Microsoft Cognitive Services provides a broad set of APIs that enable speech, language, knowledge, search and vision technologies for AI developers.

Microsoft's Cortana front-end natural language understanding (NLU) digital assistant provides another customer-facing service in line with Amazon Alexa and Google Assistant -- which also appeals to enterprise customers in some industries.

Independent software vendors building AI systems for clients generally turn to AWS and Azure cloud AI services, owing to the popularity of those providers. "They almost always will offer AWS and Azure very early in their life, as they're trying to create a business model for AI as a service," Bowles said. "That's where the people are."

Know your options for public cloud AI services

Experts agree that public cloud will be a game changer for enterprises looking to run AI workloads, but combing through each provider's AI services reveals similar capabilities. Here's what the big four cloud providers offer for AI services to enterprises:

Amazon Web Services

  • Amazon Rekognition: an image recognition service that uses deep learning to detect and compare objects and faces, for developers to add visual search functionality and image classification
  • Amazon Polly: a text-to-speech service that lets applications understand end-user voice input
  • Amazon Lex: offers automatic speech recognition and natural language understanding, based on Alexa technology, for dev teams to build conversational user interfaces, interactive applications and chatbots that recognize voice and speech
  • Amazon Machine Learning: visualization tools help developers create machine learning models and build predictions into applications based on data and advanced math algorithms

Microsoft Azure

  • Microsoft Cognitive Services: APIs that enable various capabilities based on machine intelligence: custom search functionality and labs, image and video processing; customizable speech and language models for text translation, linguistics analysis and conversation UIs; and APIs to contextualize data, build Q&As, and predict decisions
  • Azure Machine Learning: a managed service for developers to build and deploy applications with predictive analytics functionality

Google Cloud Platform

  • Google Cloud Machine Learning Engine: a service based on Google's TensorFlow that enables developers to build complex machine learning models
  • Machine Learning APIs: enables AI functionality for applications with image and video analysis, speech-to-text conversion, language translation and text analysis

IBM Bluemix

  • Watson Developer Cloud: IBM bundles Watson tools and APIs. Developers can build chatbots with natural language understanding, translate languages, perform text and vocal tone analysis, convert text to and from speech, analyze images and gain insight from data.

Google's advantage is in data access and processing, which it uses internally. In addition to the open source machine learning library TensorFlow, Google Cloud Platform's APIs enable a range of AI skills, but none more promising than the predictive analytics capabilities of its machine learning tool.

"Data is going to live at the heart of [AI],'" Koplowitz said. "If data is your goal, Google stands in a special place in the world of making data available to you."

Bowles hasn't seen Google AI adoption rise to the level of its promise -- or the level of its competitors -- even with the release of its Alexa-challenging Google Home smart home device.

"But with Google, you don't want to count them out with anything at this point, particularly in an area that's as immature as AI," he said.

The future of IBM's Bluemix cloud platform might very well depend on its AI adoption. IBM's Watson APIs open up typical machine learning, visual recognition and NLU capabilities, as well as foreign language translation and analysis of text and news stories. IBM could carve out a niche in specific industries, such as financial services -- H&R Block is among its AI adopters -- and healthcare.

The cloud war will be very much dependent on [providers] having the best vision around artificial intelligence.
Nicola Morini-Bianzinoglobal lead of the artificial intelligence practice, Accenture

IBM doesn't have a smart home device, as do AWS and Google, to reach Watson users in a natural way, Koplowitz said. "But, that being said, who's going to go out and build an oncology service based on their system? Probably not [AWS]; that's not what they do. Certainly we could see IBM investing in that," he said.

But it's not clear that specific industry needs outweigh the breadth of services offered by top cloud providers. "There are factors that will contribute to you selecting one of those cloud providers over another, however, it's rarely just industry," said Jeff Sage, cloud expert at PA Consulting.

Providers deadlocked -- for now

Despite the promise of their cloud AI services, IBM and Google must lure enterprise customers away from AWS and Azure to their platforms. With overall feature parity between the four service providers, individual providers' unique features usually apply to enterprises with a specialized need or developers experimenting with the technology.

That parity, along with the general feeling of inertia involving a cloud migration, benefits AWS, which is already the home to a wide range of enterprise applications.

"[AWS has] a great endpoint that everyone wants to program to, they have really good AI services, and that's where I'm deploying my applications already," Koplowitz said. "There's definitely some wind at their back based on the fact that they have so much momentum in the application development space already."

Sage and Brown adjust provider recommendations on a daily client-by-client basis based on AI trends and individual needs, but they agree that most new features don't move the needle toward one particular provider.

Each cloud provider has made AI a focal point, and if any breaks free from the pack in AI services and entices developers to its platform, it could very well turn the cloud computing market on its ear. "In the future, the cloud war will be very much dependent on [providers] having the best vision around artificial intelligence," Morini-Bianzino said.

And even AWS' fortification in the cloud market might not be safe, as its opposition turns to AI as a future opportunity to storm the castle.

"I think everyone's looking for a loose brick," Koplowitz said. "And AI is a heck of a sledgehammer."

Kristin Knapp is senior site editor for SearchCloudComputing. Contact her at kknapp@techtarget.com or follow @kknapp86 on Twitter. David Carty is the site editor for SearchAWS. Contact him at dcarty@techtarget.com or follow @DCartyTT on Twitter.

Next Steps

Amazon Lex opens up Alexa technology for enterprises

AI, open source technology key to IBM's future success

Machine learning technology still a puzzle for IT pros

This was last published in May 2017

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