Intellyx Whitepaper for IBM Z by Jason English
Why high-fidelity, low-latency ML data and AI inferencing can thrive on mainframe platforms like IBM Z
What will it take to move AI from pilot projects to real enterprise operation?
We’ve been pitched a fantastic vision over the past decades about the expected capabilities of artificial intelligence (AI).
Maybe AI appeared in our collective consciousness via enthusiastic TED talks, or perhaps the thread started in our favorite sci-fi shows and novels. According to some storytellers, we should be immersed in a general form of AI soon and have ‘robots with personalities’ at our service.
While that’s fun to think about, there’s much more productive work to be done by AI today. AI is being used to change the game in some of the most sophisticated arenas in business and science.
The most relevant applications of AI today involve near real-time data interpretation and decision-making—or inferencing—to meet critical business needs. We must lean on AI to make inferences about what exactly should be done to resolve complex problems, help customers, or drive new opportunities when moments matter.
- To an AI engineer, the speed and performance of how AI is trained to respond to huge volumes of incoming data at each decision point are critical.
- For a business leader, AI’s value for improving quality of service, bringing new competitive differentiation to market, and avoiding risk are paramount.
- For all enterprise constituents, the causes and effects of these critical decision points are already manifested on the mainframe—the digital backbone of the business.
What is bringing sophisticated AI work and machine learning data together on the IBM Z platform?
Download the new whitepaper here (PDF, 723K, no registration required) here: https://intellyx.com/wp-content/uploads/2021/10/IntellyxWP-BringingAI-WorkloadsToIBMZ-Oct2021JE.pdf.
