Artificial Intelligence

Exploring the Microsoft Healthcare Bot partner program

Freitag, 12. Juli 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

Ermöglichen Sie mit der neuen Funktion der Formularerkennung das Verstehen von Quittungen

Montag, 8. Juli 2019

Eins der neuesten Mitglieder des Azure KI-Portfolios, die Formularerkennung, wendet moderne Technologien des maschinellen Lernens an, um Texte, Schlüssel-Wert-Paare und Tabellen präzise aus Dokumenten zu extrahieren. Mit wenigen Beispielen kann die Formularerkennung sich an die angegebenen Dokumente anpassen – egal ob lokal oder in der Cloud.

Program Manager, Microsoft Azure

Using natural language processing to manage healthcare records

Dienstag, 25. Juni 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

Donnerstag, 13. Juni 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

Donnerstag, 13. Juni 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