It’s hard to imagine how primitive the world was just ten years ago. The mobile world was still relatively new. Machine learning was still yet to flourish and become a household term.

The mere thought of having machine learning models run on mobile devices was still the stuff of science fiction.

Of course, current mobile devices are up for the challenge. Now that it’s possible, we need to figure out the processes and governance around adding this feature into mobile apps.

Traditionally, mobile application programmers and developers work with various programming languages to construct a model with an abundant code sequence series to develop a mobile application. As a result, programmers and developers face potential issues when manually constructing a mobile application. These issues consist of long development periods, errors within codes using different programming languages, frequent maintenance of models to remain up-to-date, and a more expensive budget to onboard each developer.

With Schema stitching,  developers can create a unified GraphQL schema from multiple underlying GraphQL APIs.

This has the benefit of reducing multiple data queries for data in a single request from one schema.

In this episode,  Jeremy chats with the author of Hot Chocolate, Michael Staib, about how .NET developers can implement GraphQL schema stitching with Hot Chocolate.

Useful Links

Azure Data Studio is a cross-platform client tool with hybrid and poly cloud capabilities – now brings KQL experiences for modern data professionals.

In this episode, Julie Koesmarno shows KQL magic in Notebooks and the native KQL experiences – all in Azure Data Studio.

You’ll also learn more about the use cases for KQL experiences in Azure Data Studio and our roadmap.