Introducing Azure AI
AI has become increasingly widespread in the digital world and is often the force behind innovation. Thus, the importance of increasing your understanding and skills relating to AI has become vital for developers. While AI is a very complex area, there are several programs available which can make it more accessible. One of the most popular and widely used AI programs is Azure AI.
The goal of Azure AI is to allow organizations to easily apply AI across all their business activities from transforming production and optimizing operations, to empowering employees and engaging customers.
Azure AI has four main principles, which are to:
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Increase the productivity of developers and data scientists which allows them to build AI solutions faster.
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Enable developers and data scientists to deploy their AI solutions easily alongside their existing systems.
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Ensure the security and privacy of data used by organizations.
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Provide a comprehensive portfolio so that organizations can find answers to all their AI problems with an Azure AI service.
By adhering to these principles Microsoft has been able to make Azure AI more accessible for developers. For example, Azure AI has been able to achieve many industry breakthroughs in the areas of machine learning, vision, speech, and knowledge mining.
Azure Machine Learning
Azure machine learning service is the most promising product in Azure AI toolset. It tries to accelerate an end to end machine learning life cycle. This enables organizations to bring their machine learning models to the market quickly.
Azure ML provides both No-Code and Code-First approaches to building Machine learning models. An important feature of Azure ML is its automated zero code user interfaces which allow data scientists to create, train and deploy ML models with just a few clicks and drag and drop capabilities. Also, powerful Azure Notebooks provide a code first approach to building more complex machine learning models.
Azure MLOps is responsible for the DevOps process in a Machine Learning life cycle, from data preparation to deployment and monitoring. Azure MLOps uses machine learning pipelines to simplify workflows while taking advantage of CI/CD for ease of maintenance. Furthermore, MLOps allows you to manage your model artifacts from a central portal.
Azure ML service strives to create an open platform by contributing to open-source MLflow project providing native support in Azure machine learning service. MLflow is one of the latest open source platforms for a complete machine learning lifecycle.
Azure AI Apps
Azure AI Apps allow you to integrate AI into apps and agents. Azure cognitive service and Azure bot service are examples of Azure AI Apps. Azure cognitive service enables you to embed the ability to see, hear, respond, translate and reason, among others, into apps. The services provided by cognitive service include decision making, vision, speech, and language.
Services such as Content Moderator, Anomaly Detector and personalizer are examples of services that assist in the decision-making process. The most recent release, Personalizer improves an app’s usability and engagement by prioritizing content for each user.
Ink Recognizer is a new service which is available in preview for vision. This tool is capable of recognizing digital handwriting, the layout of inked documents and common shapes for various scenarios such as form-filling, note-taking, document annotation, and content search.
In Speech, Conversation Transcription is a service with advanced speech-to-text capabilities, such as real-time speech recognition, speaker identification, and speaker diarization. These capabilities mean that Conversation Transcription can be used for transcribing in-person meetings.
In the language section, Azure has introduced Language Understanding which can identify valuable information in conversations and interpret speakers’ intents.
How Can Azure AI Support Knowledge Mining?
Knowledge mining is the process of extracting meaningful information from different sources. These sources can range from databases to media files to files. It can be a challenging process to extract only valuable decisions from sources in order to improve the decision-making process.
However, there are two services in Azure AI to facilitate knowledge mining.
First, Azure Cognitive search helps you to discover patterns and relationships in a given content. You can use Azure Cognitive Search to extract key phrases and understand the semantics, without being a data science expert, through its powerful built-in AI capabilities.
In addition, Azure AI includes Form Recognizer which automates information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. Its benefits include,
- Simple extraction as no manual labeling of data is needed
- You get output customized in a predefined report format
- It allows flexible deployment, both on-premise and in the cloud.
- It includes enterprise-grade security
Azure is continuously investing in new products and features to achieve its mission of being the best place for AI. Therefore, Azure AI should be a vital tool for developers.
Nity DPosted May 26, 2019, 4:00 PM
Fantabulous post! Love it ! Thank you so much :)