Microsoft Azure Cognitive Services: Why Is It a Leader on the Cognitive Computing Market?

Modern society is getting the most of cognitive computing — it increases process efficiency, enables accurate data analytics and enhances customer interactions, along with a host of other benefits. However, taking advantage of those benefits is impossible without the use of full-fledged cognitive services. Microsoft Azure Cognitive Services, hosted on the Microsoft Azure cloud, is a great choice. Why? Read on to find out.

What Is Cognitive Computing?

Before we dive into Microsoft Azure Cognitive Services, let’s first figure out what cognitive computing and cognitive services are.

Cognitive computing is the use of advanced technologies to simulate how people think. The technologies it uses are AI, signal processing, machine learning, neural networks, virtual reality and similar.

What is the goal of cognitive computing? Its major goal is to facilitate the development of intelligent applications without programming expertise.

Cognitive services is a set of APIs and toolkits for the development of applications that can process and generate valuable insights out of unstructured data.

Read also: The Ultimate Guide to Enterprise AI

It can be applied widely and includes the following cases:

  • Face detection
  • Speech recognition
  • Sentiment analysis
  • Behavior-based recommendations
  • Risk assessment
  • Fraud detection

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What Is Microsoft Azure Cognitive Services?

Microsoft is frequently featured in various ratings and evaluation reports that relate to cognitive computing services. For example, it is recognized as an all-time leader by The Forrester Wave: Cognitive Search, The Forrester Wave: Computer Vision Platforms, Gartner’s Magic Quadrant for Insight Machines and, most importantly, Gartner’s Magic Quadrant for Cloud AI Developer Services.

Magic Quadrant for Cloud AI Developer Services

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Source: awscloud.com

We know from our own experience that Microsoft Azure Cognitive Services has what it takes to build efficient, robust intelligent applications, as we ourselves have used it to develop projects.

In 2015, Microsoft Corporation released a set of intelligent technologies and named them Project Oxford. It included solutions for performance of cognitive tasks such as face detection, speech recognition, image categorization and language understanding.

As the product expanded, it was rebranded as Azure Cognitive Services, and is now one of the most efficient services of the kind.

Azure Cognitive Services is a family of APIs and SDKs used to create intelligent applications by opening up capabilities of AI to everyone, including people who lack machine-learning expertise.

These tools work across all programming platforms and languages and help incorporate AI functionality into various applications with minimal effort and coding. With these APIs, developers can enrich software with the ability to see, speak, hear, understand and make decisions.

For access to Microsoft cognitive technologies, a developer must create an Azure account.

Read also: Migrating to Azure: Best Practices

Pros and Cons

It’s this product’s considerable benefits that make companies decide to adopt it. Let’s take a closer look at the advantages of cognitive services in Azure.

  • Wide range of use cases. Comprehensive domain-specific capabilities allow companies to apply the product to a variety of business purposes.
  • Human parity. Microsoft’s AI capabilities in vision, speech and language are equal to those of humans.
  • Easy, quick and flexible deployment. The container-based architecture enables flexible deployment from the cloud that doesn’t require AI expertise.

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However, there are still a couple of challenges:

  • The connection of cloud-based apps with internal data sources (a hybrid network and an integration strategy should be implemented before the start of development)
  • Availability (the product is available only in specific regions)

Main Rivals

The best-known of Microsoft’s competitors in the cognitive services domain are probably Google Cloud Platform, Amazon Web Services and IBM Watson. Although they have much in common, each has its idiosyncratic capabilities, as outlined below.

AWS

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Amazon has stated that AWS is the most frequently chosen cloud for AI and machine learning capabilities. The product “puts machine learning in the hands of every developer” and provides the following functionalities:

  • Machine learning services to quickly and easily create, train and implement ML models
  • AI services to build intelligent applications with minimal effort
  • Deep learning frameworks to make the most of neural networks with representation learning

AWS cognitive services include Amazon Lex (chatbot AI) that powers the renowned Alexa, Amazon Polly (which transforms text into speech) and Amazon Rekognition (which adds image and video analysis to applications).

Read also: Machine Learning in Business: 8 Use Cases

Google Cloud Platform

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A part of the Google cloud machine learning platform, Google’s cognitive services provide the following APIs:

  • Video AI, which extracts metadata from videos and makes them discoverable by identifying their content
  • Vision AI, which recognizes images
  • Speech-to-Text and Text-to-Speech, which converts audio into text and vice versa
  • Cloud Natural Language, which recognizes text
  • Cloud Translation, which translates text between languages

IBM Watson

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As stated by IBM, IBM Watson cognitive computing services allow companies to make more accurate predictions, streamline decision-making and work routine, and make the most out of employee load. IBM’s cognitive services platform does that with the following products and services:

  • Watson Studio, which creates models
  • Watson Machine Learning, which creates and deploys ML-based models
  • Watson OpenScale, which manages AI models
  • Watson Knowledge, which manages data assets
  • Watson Assistant, which embeds conversational interfaces into applications and devices
  • Watson Discovery, which enhances data search
  • Watson AIOps, which improves process efficiency
  • IBM OpenPages with Watson, which manages GRC

Microsoft Azure Cognitive Services: Inside-Out

As mentioned above, this product’s capabilities reach human parity. So, what exactly does Azure Cognitive Services have under the hood? Let’s explore in more detail.

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Vision

Microsoft’s Vision APIs analyze visual content (images, video and digital ink) and identify objects within it. The APIs therefore enable apps to authenticate and group faces according to specific characteristics, or to detect specified objects and details.

Vision APIs include:

  • Computer Vision. The service helps analyze and enhance the discoverability of visual content: it extracts and recognizes text, tags and categorizes images, generates descriptions, and recognizes human faces and other objects.
  • Custom Vision. The service customizes computer vision models to specific business requirements.
  • This API helps detect human faces in an image with attribute features such as age, gender, pose, smile and facial hair, and emotions.
  • Form Recognizer. This API helps detect and extract required forms of content.
  • Video Indexer. This API tracks and identifies visual content in videos, extracts audio transcriptions, detects scenes, interprets text, analyzes sentiment, searches images and events in a video, and then indexes this information.
Read also: How Artificial Intelligence and Robotics Are Changing Our Lives

Speech

Speech APIs help embed speech processing in apps: they convert speech to text and vice versa, translate text to other languages, and identify speakers. The technology can be applied in hands-free tools used to dictate text or to read instructions out loud, for instance.

Speech APIs include the following:

  • Speech to Text and Text to Speech, which helps apps transcribe audio to text and vice versa, with support for 85+ languages
  • Speech Translation, which enables the transcription and translation of conversations in real time
  • Speaker Recognition, which identifies the speaker based on audio content, with the ability to be used as a means of access control and authentication

Language

Language APIs analyze text to extract meaning from it. They include the following:

  • Immersive Reader, which helps readers pick out the meaning of the text, regardless of their abilities
  • Language Understanding, which teaches apps, smart devices and bots to understand natural language
  • QnA Maker, which helps enrich apps with question-and-answer capabilities
  • Text Analytics, which analyzes text to detect sentiment and key phrases
  • Translator, which conducts real-time machine translation with multiple-language support (more than 60 languages)

Decision

Decision APIs analyze data, and discover relationships and patterns to perform quicker, smarter and more efficient decision-making. These include the following:

  • Anomaly Detector, which identifies issues in a proactive manner
  • Content Moderator, which monitors content for offensive or unwanted materials
  • Metrics Advisor, which controls metrics and identifies problems
  • Personalizer, which helps create valuable, rich, user-specific content and recommendations and is vital for the creation of advanced user experiences
Read also: Customer Experience vs. E-Commerce. Comparison and Striking Examples.

Web Search

Search APIs enhance searching on the Internet. These include the following:

  • Bing Autosuggest, which provides type-ahead options for searches
  • Bing Custom Search, which creates tailored search experiences
  • Bing Entity Search, which recognizes and classifies entities and places and then searches for the required result
  • Bing Image Search, which is responsible for image searching
  • Bing News Search, which searches for news on the web according to the query
  • Bing Spell Check, which finds and corrects spelling errors
  • Bing Video Search, which is responsible for video searching
  • Bing Visual Search, which is responsible for image-based searching
  • Bing Web Search, which provides location-based, ad-free search results based on processing of all types of web results

Real-Life Use Cases

Let’s make ourselves familiar with some examples of applications that rely on Microsoft’s cognitive services to understand how they work in real life.

Uber

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To protect its millions of users against crime and fraud, Uber implemented the Azure Face API, which allows the app to check the driver’s identity. Passengers can be sure that their driver owns the account. This functionality helps the Uber app recognize faces regardless of lighting or a person’s pose and emotions.

Volkswagen

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Volkswagen has a large volume of content that needs to be translated between more than 40 languages every day — menus, manuals, infotainment systems and other text content. This requires billions of words to be translated with short turnaround times.

Azure’s excellent learning capabilities, flexibility and high scalability enable precise translation in near-real time and in a cost-effective manner.

BBC

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To streamline interactions with its audience and improve user experience, BBC relies on a branded voice assistant. This assistant provides content searching in BBC’s audio database, in the form of conversation. The voice assistant is based on Microsoft Azure Cognitive Services and the Azure Bot Service.

A Mobile Application for Visually Impaired Users by SaM Solutions

Our team developed a mobile app for a healthcare company. The application helps blind or visually impaired people recognize faces, items and scenes. It works as follows:

  1. The smartphone camera is used to take a picture of an object.
  2. The app uses the back-end cognitive services and all available document libraries to provide a description of the object.
  3. The app identifies the object and tells the user who or what is in the picture, via speech generation.

* Pictures of persons are taken or uploaded by the user and then added to the database.

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The application supports many languages, including English, German, French, Spanish, Portuguese, Italian, Chinese, Arabic, Japanese, Greek, Dutch and Hungarian.

Read also: Web Accessibility Initiative: Developing Accessible Software

Microsoft Azure: Human Parity Cognition

With Microsoft Azure Cognitive Services, AI and machine learning have become much more accessible to enterprises. Its APIs eliminate the need to build costly infrastructure as developers can implement Azure Cognitive Services using the same code for different ecosystems and without much learning.

SaM Solutions has broad experience in working both with the Microsoft Azure cloud platform and its Cognitive Services APIs. Our developers have implemented AI elements, as well as Bing Speech API, Language Understanding Intelligent Service, Computer Vision API and Video Indexer, into many projects. Contact us to learn more about Microsoft’s cognitive services or to create an efficient application that makes the most of them.

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About the author

Yuliya Shaptunova
Yuliya Shaptunova

A content writer at SaM Solutions, Yuliya is anxious to create and deliver relevant experiences. She evangelizes corporate knowledge on expertise and innovations that the company provides.