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Survey Finds Many Companies Do Little or No Management of Cloud Spending  

A heat map showing peaks and valleys of cloud computer use can be used to set schedules for better management of costs. (Credit: Getty Images)   
By John P. Desmond, AI Trends Editor  
Cloud computing “sticker shock” is on the rise as the monthly bills come in, the customers may not be sure what they are paying for, and the bills are trending upwards. 
J.R. Storment, executive director, the FinOps Foundation
That was a finding of a recent survey by the FinOps Foundation, a non-profit trade association focused on cloud financial management best practices, of more than 800 FinOps practitioners spending $45 …

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How to Meet the Enterprise-Grade Challenge of Scaling AI 

Scaling AI for the enterprise involves challenges around customization, data, talent and trust, suggest experts with experience. (Credit: Getty Images) 
By AI Trends Staff  
Organizations that have made a commitment to developing AI projects and have experienced some success next face the challenges around successfully scaling the project for the enterprise.   
To experience all the benefits, the organization needs to align the AI to the business strategy, ensure cross-functional collaboration, invest in the right talent and training, and apply strong data practices, suggests a recent account in Tech Wire.   
These are no small tasks. A recent global survey on AI …

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Time is Right for the AI Infrastructure Alliance to Better Define Rules 

The IT Infrastructure Alliance was founded to bring more structure and discipline to AI development by defining the rules more clearly.  
By John P. Desmond, AI Trends Editor  
The AI Infrastructure Alliance is taking shape, adding more partners who sign up to the effort to define a “canonical stack for AI and Machine Learning Operations (MLOps).” In programming, “canonical means according to the rules,” from a definition in webopedia.   
The mission of the organization also includes, according to its website: develop best practices and architectures for doing AI/ML at scale in enterprise organizations; foster openness for algorithms, tooling, libraries, …

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