AI News, IBM Watson Now Available Anywhere artificial intelligence

With Watson Anywhere, IBM wants to democratize artificial intelligence

According to a recent PwC study, AI adoption is poised to inject a potential $16 trillion to the global economy over the next decade.

Add to that the common practice of AI vendor lock-in, wherein AI can only be used for data housed in a particular vendor’s platform, and the four percent adoption rate starts to make a bit more sense.

“We think this is going to be absolutely game-changing for AI.” IBM has built a series of new microservices on IBM Cloud Private for Data, their AI information architecture, to allow users to run Watson on any “public, hybrid or multi-cloud environment, enabling businesses to infuse AI into their apps, wherever they reside.” But while this may address the issue of vendor lock-in, AI-powering their businesses continues to be a pipe dream for firms struggling with a lack of talent needed to this new technology.

“We really believe these things combined will help our clients have access to the talent they need to leverage all their data science tools.” With these new services and educational programs, IBM hopes to build a path for firms looking to infuse their businesses with AI, regardless of the expertise they currently command.

“It requires an open approach to technology, a philosophy of bringing the best AI to the data, and a commitment to educating the world in this 21st century skill.” “We are freeing clients from AI vendor lock-in and helping them begin to unlock insights from their data – wherever it resides, taking one big step towards AI everywhere,” he said.

IBM Think 2019: Takeaways on the Promise of Cloud-Portable Artificial Intelligence

That’s good news for many long-time IBM customers, but I have to wonder whether more heterogeneous firms or entirely new customers will be attracted to what seemed like an all-IBM offering designed for multi-cloud deployment?

You can take more of an a la carte approach, as I discuss below, but execs did a better job of detailing the IBM stack than they did of explaining the modularity and flexibility of the portfolio to work with the components and services that customers might use on other public clouds.

In my recent case study report on Royal Dutch Shell, Daniel Jeavons, the company’s general manager of data science, explained that the energy giant is using both AWS and Azure, tapping what it thinks of as interchangeable, but low-cost and quickly scalable information architecture available on each cloud.

So in Shell’s case, the IA is seen as interchangeable while the AI and data-science capabilities – and the models developed thereon – are the prized part that the company wants available in hybrid- and multi-cloud fashion, applicable wherever data lives.

To me, some of the more attractive components of the overall stack are Watson Studio, which includes a nice collection of open source frameworks, and Watson OpenScale, which addresses the monitoring and optimization aspects of the model-development-and-deployment lifecycle often neglected in data science offerings.

Source: IBM The advantage of a more a la carte approach would be, for example, bringing a consistent data science and model-management environment to a range of deployments, drawing on data where it lives (assuming it’s already on that cloud) rather than moving it or replicating it onto a separate platform (ICP/D running on IaaS).

But if you’re going to talk up multi-cloud support, in my book you might as well talk up the ability to work with data where it lives and bring what the customer loves most (and not everything) to a third-party cloud.

I wouldn't expect IBM execs to talk up the components and services customers might want to use on other clouds, but it would have helped the deploy-anywhere story to hear more about the modularity of the portfolio and the availability of REST APIs and more to play nicely on third-party clouds.

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