“Machine learning (ML) today is frustrating. There is so much potential and the algorithms are all there, but I just do not know how I can leverage it for my organization,” says the CTO of a major professional services firm. “My CEO wants me to ‘leverage ML to lower OPEX and differentiate our service offerings, but there is nothing out there in the market that would allow me to get this done in a manner that has a high probability of success.” Then of course he asks me what I would do and where I would start, because the guy with “Machine Learning and AI” in his job title must know for sure…
Top 3 Guidelines for Leveraging Machine Learning and Artificial Intelligence to Lower OPEX and Increase Competitiveness
Topics:
Amazon Web Services
Microsoft
Artificial intelligence
AI
E-Commerce
ML
Machine Learning
Advanced Analytics
AI for Business
AIOPS
Adaptive optimization
Alexa Internet
Amazon.com
Best Practices
Boston Consulting Group
Business Insider
IT Automation
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Machine Learning and Artificial Intelligence: The Promised Land for Lowering IT OPEX, Decreasing Operational Risk and Optimally Supporting Business Goals
What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'
Machine Learning and Artificial Intelligence: The Promised Land for Lowering IT OPEX, Decreasing Operational Risk and Optimally Supporting Business Goals
What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'
Topics:
Containers
IT Management
Artificial intelligence
IT operations
DevOps
AI
Container Management
ML
Machine Learning
OPEX
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