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Cloud-based generative AI systems that use too many resources turn out to be too complex and expensive. Here’s how you can avoid this.
In the ramp-up for AI, companies feel pressured to choose cloud or on-premises. Let’s learn from past mistakes and realize the solution is rarely one size fits all.
At Build, Microsoft described how Azure is supporting large AI workloads today, with an inference accelerator, high-bandwidth connections, and tools for efficiency and reliability.
Rapid cloud adoption has left many enterprises needing help with their technology infrastructure. These simple rules will keep the pain to a minimum.
There is widespread fear in the securities and finance sectors that using generative AI will force companies to rely on giant cloud companies.
Microsoft delivers a one-stop shop for big data applications with its latest updates to its data platform.
CISOs are still hampered by bad assumptions and outdated approaches. They should be involved in decisions from day 1 to address unique business needs.
AI agents offer flexibility and autonomy as they plan and complete complex tasks that traditionally require human involvement.
Major update to Copilot Studio gives us something much closer to an AI layer for the Power Platform, linking modern AI tools to process automation.
The explosive growth of generative AI drives the multicloud model. But be prepared because it’s going to cost more money.
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