I've posted several examples here of using Azure Cognitive Services for data science applications. You can upload an an image or video to the service and extract information about faces and emotions, generate a caption describing a scene from a provided photo, or speak written text in a natural voice. (If you haven't tried the Cognitive Services tools yet, you can try them out using the instructions in this notebook using only a browser.)
But what if you can't upload an image or text to the cloud? Sending data outside your network might be subject to regulatory or privacy policies. And if you could analyze the images or text locally, your application could benefit from reduced latency and bandwidth.
Now, several of the Azure Cognitive Services APIs are available as Docker containers: you can download a container that provides the exact same APIs as the cloud-based services, and run it on a local Linux-based server or edge device. Images and text are processed directly in the container and never sent to the cloud. A connection to Azure is required only for billing, which is at the same rate as the cloud-based services (including a free tier).
The services now available as containers are:
- Face: face detection, identity verification, and emotion detection. In private preview: sign up here. Free and paid tier details here.
- Recognize text: detect and extract printed text from images. In private preview: sign up here. Free and paid tier details here.
- Text Analytics: Key phrase extraction, language detection, and sentiment analysis. In public preview, available to all Azure subscribers. Free and paid tier details here.
You can learn more about Cognitive Services in the video below. (The information about the new container support starts at 11:20.)
You can also find detailed information about the Cognitive Services in containers in the blog post linked below.
Microsoft Azure blog: Getting started with Azure Cognitive Services in containers