Artificial Intelligence

Conversational AI updates for July 2019

2019년 7월 18일 목요일

With the Bot Framework release in July, we are happy to share new releases of Bot Framework SDK 4.5 and preview of 4.6, updates to our developer tools, and new channels in Azure Bot Service. We’ll use the opportunity to provide additional updates for the Conversational AI releases from Microsoft.

Principal Program Manager, Azure Platform

Exploring the Microsoft Healthcare Bot partner program

2019년 7월 12일 금요일

Every day, healthcare organizations are beginning their digital transformation journey with the Microsoft Healthcare Bot Service built on Azure. The Healthcare Bot service empowers healthcare organizations to build and deploy an Artificial Intelligence (AI) powered, compliant, conversational healthcare experience at scale.

Product Manager - Microsoft Healthcare

Using natural language processing to manage healthcare records

2019년 6월 25일 화요일

The next time you see your physician, consider the times you fill in a paper form. It may seem trivial, but the information could be crucial to making a better diagnosis. Now consider the other forms of healthcare data that permeate your life—and that of your doctor, nurses, and the clinicians working to keep patients thriving.

Principal Healthcare Program Manager, Industry Experiences

Customers get unmatched security with Windows Server and SQL Server workloads in Azure

2019년 6월 13일 목요일

Customers such as Allscripts, Chevron, J.B. Hunt, and thousands of others are migrating their important workloads to Azure where they find unmatched security. While understanding cloud security is initially a concern to many, after digging in, customers often tell us the security posture they can set up within Azure is easier to implement and far more comprehensive than what they can provide for in other environments.

General Manager, Microsoft Azure

Make your data science workflow efficient and reproducible with MLflow

2019년 6월 13일 목요일

When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files. They often try different models and parameters, for example random forests of varying depth, linear models with different regularization rates, or deep learning models with different architectures trained using different learning rates.

Senior Program Manager