Artificial Intelligence

Exploring the Microsoft Healthcare Bot partner program

vendredi 12 juillet 2019

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

Comprendre le traitement des reçus avec la nouvelle fonctionnalité de Form Recognizer

lundi 8 juillet 2019

Parmi les membres les plus récents du portefeuille Azure AI, Form Recognizer utilise une solution de Machine Learning avancée pour extraire avec précision du texte, des paires clé-valeur et des tables à partir de documents. Avec juste quelques exemples, il adapte sa compréhension aux documents fournis, aussi bien en local que dans le cloud.

Program Manager, Microsoft Azure

Using natural language processing to manage healthcare records

mardi 25 juin 2019

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

jeudi 13 juin 2019

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

jeudi 13 juin 2019

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