AI News, Introducing Textile — A machine learning platform for mobile apps.

Introducing Textile — A machine learning platform for mobile apps.

Today, we are excited to release Textile, a new platform that dramatically simplifies the development of machine learning and artificial intelligence for mobile apps.

Collecting raw data from mobile apps disconnects the data science workflow from the source of data, often making steps to improve collection and engineer new data features slow and cumbersome.

After data has been collected and modeled, normally at the end of a giant data warehousing and ETL pipeline, those models are next to impossible to run in a mobile app because the features they are trained with are not available on the device.

If you are a data scientist working on a mobile app (or looking to expand your data science capabilities) and wish you could spend less time cleaning and processing data and more time coming up with interesting questions to ask (and then actually answering them!), come check out Textile or drop us a line and let us know what you are working on.

Visualize real-time sensor data from your Azure IoT hub by using the Web Apps feature of Azure App Service

In this tutorial, you learn how to visualize real-time sensor data that your IoT hub receives by running a web application that is hosted on a web app.

To add a consumer group to your IoT hub, follow these steps: On GitHub, we've made available a web application that displays real-time sensor data from your IoT hub.

All you need to do is configure the web app to work with a Git repository, download the web application from GitHub, and then upload it to Azure for the web app to host.

Enterprise Mobility

Indusa developed an inventory inspection mobile application for Arvind, which automated their manual inspection process and improved the accuracy of inventory management.

The inventory inspection mobile app empowers inspection officers, warehouse users, and inventory managers to enter inventory data while on location at the warehouse, essentially eliminating their paper-based process.

During the inspection, there is a field in the app to enter in additional notes, including item location, consignment number, actual physical availability, and quantity.

At the conclusion of the inspection, the app automatically generates reports about the entire list of inspected items, including mismatched data and new items found/recorded.

The inspection app has a mechanism that will automatically create a request queue based on the total number of records and the background scheduler will automatically fetch data accordingly.

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