What’s new in Azure Data, AI, and Digital Applications: Are you ready to go from GenAI experimentation to solutions deployed at scale?
We are now in the second year of the era of AI.
We are now in the second year of the era of AI.
This month, I’m pleased to expand this blog’s scope and bring in what’s new for digital applications for a holistic look at everything we’re delivering to help customers modernize their data estate, build intelligent applications, and apply AI technologies to help achieve their business goals.
Welcome to Microsoft Ignite 2023! The past year has been one of true transformation.
As a leader in all things AI, Microsoft has spearheaded a curriculum created especially for you and your colleagues to help you build the knowledge, insights, and skills needed to make the most of AI technologies.
To learn more about how to achieve efficiency and maximize cloud value with Azure, join us at Securely Migrate and Optimize with Azure digital event on Wednesday, April 26, 2023.
Modernizing its data platform helped the tax-prep giant unleash innovations that have positioned the company for the future.
We’re thrilled to have the NBA partner with Azure to modernize apps and deliver memorable customer experiences time and time again.
The Microsoft Intelligent Data Platform, empowers organizations to invest more time creating value rather than integrating and managing their data estate.
Cloud adoption increased significantly during COVID-19 and continues for many companies. However, an enormous migration and modernization opportunity remains as organizations continue their digital transformation.
The global events of the last couple of years have introduced significant changes to how companies operate and the way we work, accelerating digital transformation for many as they seek the additional flexibility, scale, and cost savings of the cloud.
It’s clear that the fragmentation which exists today between databases, analytics, and governance products must be addressed.
With the advance of AI and machine learning, companies start to use complex machine learning pipelines in various applications, such as recommendation systems, fraud detection, and more.