Azure Applied AI Services
Author: Ronald Fung
Creation Date: May 10, 2023
Next Modified Date: May 10, 2024
A. Introduction
Azure Applied AI Services are high-level services focused on empowering developers to quickly unlock the value of data by applying AI into their key business scenarios. Built on top of the AI APIs of Azure Cognitive Services, Azure Applied AI Services are optimized for critical tasks ranging from monitoring and diagnosing metric anomalies, mining knowledge from documents, enhancing the customer experience through transcription analysis, boosting literacy in the classroom, document understanding and more. Previously, companies would have to orchestrate multiple AI skills, add business logic, and create a UI to go from development to deployment for their scenario – all of which would consume time, expertise, and resources – these “scenario specific” services provide developers these benefits “out of the box”.
B. How is it used at Seagen
As a biopharma research company using Microsoft Azure, you can use Azure Applied AI Services to build intelligent applications and services that can help you to automate processes, gain insights from your data, and make better decisions. Here are some ways you can use Azure Applied AI Services:
Cognitive Services: Azure Cognitive Services provides a set of pre-built APIs that can help you to add intelligent features to your applications, such as speech recognition, computer vision, natural language processing, and sentiment analysis. This can help you to automate processes, improve customer experience, and gain insights from your data.
Bot Services: Azure Bot Services provides a platform for building and deploying intelligent bots that can interact with your customers and employees through natural language. This can help you to automate customer service, reduce response times, and improve engagement.
Machine Learning: Azure Machine Learning provides a platform for building and deploying machine learning models. This can help you to gain insights from your data, make predictions, and automate processes.
Azure Databricks: Azure Databricks provides a collaborative platform for building and deploying big data and machine learning applications. This can help you to gain insights from your data, improve decision-making, and automate processes.
Azure Stream Analytics: Azure Stream Analytics provides a real-time analytics service that can help you to process and analyze streaming data from various sources. This can help you to gain insights from your data in real-time and automate processes.
Overall, Azure Applied AI Services can help your biopharma research company to build intelligent applications and services that can help you to automate processes, gain insights from your data, and make better decisions. With Cognitive Services, Bot Services, Machine Learning, Azure Databricks, and Azure Stream Analytics, Azure Applied AI Services can help you to add intelligence to your applications and services and improve your business outcomes.
C. Features
Azure Form Recognizer
Enabling organizations in all industries to consume information hidden within documents to increase productivity, automate business processes and generate knowledge and insights. Azure Form Recognizer is a service that lets you build automated data processing software using machine learning technology. Identify and extract text, key/value pairs, selection marks, tables, and structure from your documents. The service outputs structured data that includes the relationships in the original file, bounding boxes, confidence and more. You quickly get accurate results that are tailored to your specific content without heavy manual intervention or extensive data science expertise. Use Form Recognizer to automate data entry in your applications and enrich your documents’ search capabilities. Azure Form Recognizer is built using OCR, Text Analytics and Custom Text from Azure Cognitive Services.
Form Recognizer is composed of custom document processing models, prebuilt models for invoices, receipts, IDs and business cards, and the layout model.
Azure Metrics Advisor
Protecting organization’s growth by enabling them to make the right decision based on intelligence from metrics of businesses, services and physical assets. Azure Metrics Advisor uses AI to perform data monitoring and anomaly detection in time series data. The service automates the process of applying models to your data, and provides a set of APIs and a web-based workspace for data ingestion, anomaly detection, and diagnostics - without needing to know machine learning. Developers can build AIOps, predictive maintenance, and business monitoring applications on top of the service. Azure Metrics Advisor is built using Anomaly Detector from Azure Cognitive Services.
Azure Cognitive Search
Unlock valuable information lying latent in all your content in order to perform an action or make decisions. Azure Cognitive Search is the only cloud search service with built-in AI capabilities that enrich all types of information to help you identify and explore relevant content at scale. Use cognitive skills for vision, language, and speech, or use custom machine learning models to uncover insights from all types of content. Azure Cognitive Search also offers semantic search capability, which uses advanced machine learning techniques to understand user intent and contextually rank the most relevant search results. Spend more time innovating and less time maintaining a complex cloud search solution. Azure Cognitive Search is built using Computer Vision and Text Analytics from Azure Cognitive Services.
Azure Immersive Reader
Enhance reading comprehension and achievement with AI. Azure Immersive Reader is an inclusively designed tool that implements proven techniques to improve reading comprehension for new readers, language learners, and people with learning differences such as dyslexia. With the Immersive Reader client library, you can leverage the same technology used in Microsoft Word and Microsoft OneNote to improve your web applications. Azure Immersive Reader is built using Translation and Text to Speech from Azure Cognitive Services.
Azure Bot Service
Enable rapid creation of customizable, sophisticated, conversational experiences with pre-built conversational components enabling business value right out of the box. Azure Bot Service Composer is an open-source visual authoring canvas for developers and multidisciplinary teams to build bots. Composer integrates language understanding services such as LUIS and QnA Maker and allows sophisticated composition of bot replies using language generation. Azure Bot Service is built using Speech/Telephony, LUIS, and QnA Maker from Azure Cognitive Services.
Azure Video Analyzer
Enabling businesses to build automated apps powered by video intelligence without being a video or AI expert. Azure Video Analyzer is a service for building AI-based video solutions and applications. You can generate real-time business insights from video streams, processing data near the source and applying the AI of your choice. Record videos of interest on the edge or in the cloud and combine them with other data to power your business decisions. Azure Video Analyzer is built using Spatial Analysis from Azure Cognitive Services. Azure Video Analyzer for Media is built using Face, Speech, Translation, Text analytics, Custom vision, and textual content moderation from Azure Cognitive Services.
Certifications and compliance
Applied AI Services has been awarded certifications such as CSA STAR Certification, FedRAMP Moderate, and HIPAA BAA. You can download certifications for your own audits and security reviews.
To understand privacy and data management, go to the Trust Center.
D. Where implemented
E. How it is tested
Testing Azure Applied AI Services involves ensuring that the services are functioning correctly, securely, and meeting the needs of all stakeholders involved in the project. Here are some steps to follow to test Azure Applied AI Services:
Define the scope and requirements: Define the scope of the project and the requirements of all stakeholders involved in the project. This will help ensure that Azure Applied AI Services are designed to meet the needs of all stakeholders.
Develop test cases: Develop test cases that cover all aspects of Azure Applied AI Services functionality, including natural language processing, speech recognition, image analysis, and machine learning. The test cases should be designed to meet the needs of the organization, including scalability and resilience.
Conduct unit testing: Test the individual components of Azure Applied AI Services to ensure that they are functioning correctly. This may involve using tools like PowerShell or Azure CLI for automated testing.
Conduct integration testing: Test Azure Applied AI Services in an integrated environment to ensure that they work correctly with other systems and applications. This may involve testing Azure Applied AI Services with different operating systems, browsers, and devices.
Conduct user acceptance testing: Test Azure Applied AI Services with end-users to ensure that they meet their needs and are easy to use. This may involve conducting surveys, interviews, or focus groups to gather feedback from users.
Automate testing: Automate testing of Azure Applied AI Services to ensure that they are functioning correctly and meeting the needs of all stakeholders. This may involve using tools like Azure DevOps to set up automated testing pipelines.
Monitor performance: Monitor the performance of Azure Applied AI Services in production to ensure that they are meeting the needs of all stakeholders. This may involve setting up monitoring tools, such as Azure Monitor, to track usage and identify performance issues.
Address issues: Address any issues that are identified during testing and make necessary changes to ensure that Azure Applied AI Services are functioning correctly and meeting the needs of all stakeholders.
By following these steps, you can ensure that Azure Applied AI Services are tested thoroughly and meet the needs of all stakeholders involved in the project. This can help improve the quality of Azure Applied AI Services and ensure that they function correctly in a production environment.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
Testing Azure Applied AI Services involves ensuring that the services are functioning correctly, securely, and meeting the needs of all stakeholders involved in the project. Here are some steps to follow to test Azure Applied AI Services:
Define the scope and requirements: Define the scope of the project and the requirements of all stakeholders involved in the project. This will help ensure that Azure Applied AI Services are designed to meet the needs of all stakeholders.
Develop test cases: Develop test cases that cover all aspects of Azure Applied AI Services functionality, including natural language processing, speech recognition, image analysis, and machine learning. The test cases should be designed to meet the needs of the organization, including scalability and resilience.
Conduct unit testing: Test the individual components of Azure Applied AI Services to ensure that they are functioning correctly. This may involve using tools like PowerShell or Azure CLI for automated testing.
Conduct integration testing: Test Azure Applied AI Services in an integrated environment to ensure that they work correctly with other systems and applications. This may involve testing Azure Applied AI Services with different operating systems, browsers, and devices.
Conduct user acceptance testing: Test Azure Applied AI Services with end-users to ensure that they meet their needs and are easy to use. This may involve conducting surveys, interviews, or focus groups to gather feedback from users.
Automate testing: Automate testing of Azure Applied AI Services to ensure that they are functioning correctly and meeting the needs of all stakeholders. This may involve using tools like Azure DevOps to set up automated testing pipelines.
Monitor performance: Monitor the performance of Azure Applied AI Services in production to ensure that they are meeting the needs of all stakeholders. This may involve setting up monitoring tools, such as Azure Monitor, to track usage and identify performance issues.
Address issues: Address any issues that are identified during testing and make necessary changes to ensure that Azure Applied AI Services are functioning correctly and meeting the needs of all stakeholders.
By following these steps, you can ensure that Azure Applied AI Services are tested thoroughly and meet the needs of all stakeholders involved in the project. This can help improve the quality of Azure Applied AI Services and ensure that they function correctly in a production environment.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture