To build solutions with machine learning, requires a data scientist. Now Microsoft is enabling the cognitive services to take advantage of AI with developers, without requiring a data scientist. This is happening by getting machine learning models and the pipelines and the infrastructure needed to build a model and packaging it up into a Cognitive Service for vision, speech, search, text processing, language understanding, and more.
The advantage of this scenario is it is possible for anyone who can write a program to now use machine learning to improve an application. But if the developer tries to create Large scale applications using AI they face many problems on that. To overcome the problems Microsoft is introducing container support for Cognitive Services, making it significantly easier for developers to build ML-driven solutions.
This allows developers to build big AI systems that run at scale, reliably, and consistently in a way that supports better data governance.
Let’s take an example:
Assume a typical hospital system with some patients. After sometime there might be number of notes, records or files that they use daily. Using Cognitive Services containers, they can process all of these files, index millions of documents and find commonalities, and improve the patient experience while keeping the data in-house.
There are 5 key capabilities within Azure Cognitive Services.
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Text Analytics Containers
- Key Phrase Extraction
This can extract main key words to identify main points such as for the input text "The food was delicious and there were wonderful staff," the API returns the main talking points: "food" and "wonderful staff."
- Language Detection
Language support is tremendous and it supports up to 120 languages, and detects the language input text is written in and reports a single language code for every document submitted on the request.
- Sentiment Analysis
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.
The Face Container enables you to add face detection, verification, and emotion detection to an application or system. It uses a common configuration framework, so that you can easily configure and manage storage, logging and telemetry, and security settings for your containers.
Recognize Text Container
The Recognize Text portion of Computer Vision allows you to detect and extract printed text from images of various objects with different surfaces and backgrounds, such as receipts, posters, and business cards.
Custom Vision Service support for logo detection
Custom Vision Service will add support for logo detection, allowing business to create their own logo detector quickly and easily. Logo detection is a specialized type of object detection suited specifically for logos that can be small, skewed, or obfuscated within a larger picture, for example on the sidelines of a soccer match, on a building sign in a cityscape, or on a scanned form. Now you can build your own logo detectors to help search and locate their logos in their media libraries or to generate analytics for their social media feeds.