Mark Davison is at IBM Think in San Francisco – It is a message that has been driven home constantly, almost mantra-like, at this year’s Think: There can be no AI (artificial intelligence) without IA (information architecture).
And, says Sumit Gupta, vice-president: HPC, AI and machine learning at IBM Cognitive Systems, enterprise leaders looking to drive business value from AI require an infrastructure composed of AI-optimised hardware and software that breaks performance barriers while also delivering AI insights when, and where, they want them.
But, Gupta adds, while the potential of AI to revolutionise a business is no longer a fantasy, there are still significant barriers to adoption. One of the largest is the lack of skills within an organisation to exploit AI. According to Gartner’s 2019 CIO Survey, when asked to provide the top three challenges to adopting AI by organisations, 54% of respondents cited a “lack of necessary staff skills” and 27% cited the “complexity of integrating AI with our existing infrastructure.
“IBM has heard similar feedback from our clients, which is why today we’re bringing together AI capabilities from IBM Watson with the AI infrastructure of IBM Systems to ease the barriers to enterprise AI adoption,” Gupta says. “I am delighted to reveal the new Watson Machine Learning Accelerator (WML Accelerator), a new piece of Watson Machine Learning (WML) designed to help enterprises train and deploy machine learning models built in IBM Watson Studio and monitored with IBM Watson OpenScale.
“The power of IBM’s AI strategy is in how we approach AI from end-to-end, including our belief that the foundation of AI is co-optimised hardware and software. When clients leverage purpose-built infrastructure designed, optimised and accelerated for AI, they open themselves up to potential performance gains that can help their business achieve faster insights and support larger enterprise-scale AI projects.”
Gupta says the company validated that approach at last year’s Think when it demonstrated the performance capabilities of SnapML machine learning library running on Power Systems servers to beat Google Cloud in running machine learning on an advertising-focused dataset by 46x, setting a new record for the tera-scale dataset.
“Since then, IBM researchers have been hard at work making SnapML a better tool for the enterprise,” Gupta explains. “By integrating new automation features, IBM is making machine learning more accessible for enterprise users that may not have the ninja data scientists on staff to cut down on time intensive, but necessary, tasks in the machine learning workflow like model selection and hyperparameter tuning. By scaling out across a cluster, as well as scaling up across many-core CPUs and powerful modern GPUs, SnapML is designed to identify an accurate model and its hyper-parameter configuration in a timely fashion to help enterprises potentially gain a competitive edge.”
Simon Thompson, research computing infrastructure architect at the University of Birmingham, adds: “Many users don’t realise how vast the open source machine learning catalogue is, and it can be quite challenging to identify the right tool for your particular data or desired outcome. The automated model and library selection capabilities of SnapML greatly reduce the time required to parse through all of these tools, allowing users to begin ML training much more quickly.”
Gupta says that with these new tools, IBM Research created a SnapML-based auto machine learning framework and ran it across five datasets that illustrated enterprise use cases: like predicting the likelihood of a traveler in missing their flight, predicting the likelihood of someone clicking on an online ad, predicting the optimal salary for a job applicant, and in a more fun but rigorous dataset, predicting the likelihood of five random playing cards turning out to be a valid poker hand.
“We ran this SnapML-based framework on a cluster of four IBM Power Systems AC922 servers, each equipped with two 20-core IBM POWER9 CPUs and four GPUs,” he says. “For comparison, two leading open source automated machine learning frameworks were deployed on the exact same configuration. Based on our own internal observations, we saw that the SnapML-based framework was able to reach a specific accuracy level 10x or faster than the compared competing frameworks across all five datasets.
“We believe that a singular, cross-IBM AI strategy will best position our clients to deliver AI everywhere,” Gupta says. “WML Accelerator is the first time that IBM has designed an integrated AI solution across IBM Watson and IBM Power Systems, unifying IBM’s best AI software with IBM’s best AI hardware. In our effort to make AI available anywhere, we’re also announcing IBM Cloud Private (ICP) for Data on IBM Power Systems with IBM Storage.
“Coupled with Watson on ICP for Data, we’re opening up possibilities for customers to leverage AI, where they want it, when they want it, and with differentiated performance to give them a competitive edge,” Gupta says.