IBM Eases Customers’ Path to AI and Hadoop
Hardware/software/data integration and interdependencies are important for enterprise workloads but are especially critical when it comes to performance-sensitive applications, such as artificial intelligence (AI). Unfortunately, they’re also easy points to misunderstand when highly complex technologies are in the early stages of commercial development and deployment like, again, AI.
As a result, if or when organizations move to or beyond AI proof of concept (PoC) exercises without clearly knowing the challenges and risks they face, it’s all too easy for them to run into problems, fail unnecessarily, then scale-back or abandon their efforts. So, it’s great when vendors help customers anticipate and steer clear of avoidable pitfalls with solutions designed to contend with and overcome fundamental technological complexities.
Those points came to mind regarding new offerings from the IBM Cognitive Systems and IBM Analytics groups. Let’s consider those announcements separately, along with how they’ll affect the company’s analytics, AI and other offerings, and customers’ related efforts.
IBM Power Systems – A reference architecture for AI
Despite the considerable hype being directed at AI’s commercial possibilities, the vast majority of businesses are still in very early stages of exploring the technology and its potential impact on their businesses. There are multiple reasons for this but prime among them is the complexity of most solutions, including hardware/software stacks and workflow/data flow processes.
As a result, pursuing and succeeding in AI requires technological sophistication and “roll your own” IT skills that are beyond the capabilities of many, if not most companies. To help address those issues, IBM’s Cognitive Systems group introduced the first iteration (v 1.1) of PowerAI Enterprise and a related reference architecture for on-premises AI deployments.
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NOTE: This column was originally published in the Pund-IT Review.