Detects human faces in images, and identifies attributes, including face landmarks (such as noses and eyes), gender, age, and other machine-predicted facial features. In addition to detection, Face can check if two faces in the same image or different images are the same by using a confidence score, or compare faces against a database to see if a similar-looking or identical face already exists. It can also organize similar faces into groups, using shared visual traits. Request access
Loads a trained or published Language Understanding model, also known as a LUIS app, into a docker container and provides access to the query predictions from the container’s API endpoints. You can collect query logs from the container and upload these back to the LUIS portal to improve the app’s prediction accuracy.
Extracts key phrases to identify the main points. For example, for the input text “The food was delicious and there were wonderful staff”, the API returns the main talking points: “food” and “wonderful staff”.
For up to 120 languages, detects which language the input text is written in and report a single language code for every document submitted on the request. The language code is paired with a score indicating the strength of the score.
Analyzes raw text for clues about positive or negative sentiment. This API returns a sentiment score between 0 and 1 for each document, where 1 is the most positive. The analysis models are pre-trained using an extensive body of text and natural language technologies from Microsoft. For selected languages, the API can analyze and score any raw text that you provide, directly returning results to the calling application.
Cloud Monitoring specialist, with more than 10 years of experience in providing brode wide monitoring solutions for SMP and Enterprises.
I work at CloudValley, Microsoft's partner of the year 2015&2017 and largest MSP in Israel.