Designing and Implementing a Microsoft Azure AI Solution

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Related Course
Durations: 5 Days

Designing and Implementing a Microsoft Azure AI Solution

Course Overview:

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. The course will use C# or Python as the programming language.

Course Objectives:

  • Describe considerations for AI-enabled application development
  • Create, configure, deploy, and secure Azure Cognitive Services
  • Develop applications that analyze text
  • Develop speech-enabled applications
  • Create applications with natural language understanding capabilities
  • Create QnA applications
  • Create conversational solutions with bots
  • Use computer vision services to analyze images and videos
  • Create custom computer vision models
  • Develop applications that detect, analyze, and recognize faces

Who Should Attend?

Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.

Course Prerequisites

There are no prerequisites for this course.

Course Content:

Module 1: Prepare to develop AI solutions on Azure
Define artificial intelligence
Understand AI-related terms
Understand considerations for AI Engineers
Understand considerations for responsible AI
Understand capabilities of Azure Machine Learning
Understand capabilities of Azure AI Services
Understand capabilities of Azure OpenAI Service
Understand capabilities of Azure AI Search

Module 2: Create and consume Azure AI services
Create Azure AI services resources in an Azure subscription.
Identify endpoints, keys, and locations required to consume an Azure AI services resource.
Use a REST API and an SDK to consume Azure AI services.

Module 3: Secure Azure AI Services
Consider authentication for Azure AI services
Manage network security for Azure AI services

Module 4: Monitor Azure AI services
Monitor Azure AI services costs.
Create alerts and view metrics for Azure AI services.
Manage Azure AI services diagnostic logging.

Module 5: Deploy Azure Ai services in containers
Create containers for reuse
Deploy to a container and secure a container
Consume Azure AI services from a container

Module 6: Analyze images
Provision an Azure AI Vision resource
Analyze an image
Generate a smart-cropped thumbnail

Module 7: Classify images
Provision Azure resources for Azure AI Custom Vision
Understand image classification
Train an image classifier

Module 8: Detect, analyze, and recognize faces
Identify options for face detection, analysis, and identification
Understand considerations for face analysis
Detect faces with the Azure AI Vision service
Understand capabilities of the Face service
Compare and match detected faces
Implement facial recognition

Module 9: Read Text in images and documents with the Azure AI Vision Service
Read text from images using OCR
Use the Azure AI Vision service Image Analysis with SDKs and the REST API
Develop an application that can read printed and handwritten text

Module 10: Analyze video
Describe Azure Video Indexer capabilities
Extract custom insights
Use Azure Video Indexer widgets and APIs

Module 11: Analyze text with Azure AI Language
Detect language from text
Analyze text sentiment
Extract key phrases, entities, and linked entities

Module 12: Build a question answering solution
Understand question answering and how it compares to language understanding
Create, test, publish and consume a knowledge base
Implement multi-turn conversation and active learning
Create a question answering bot to interact with using natural language

Module 13: Build a conversational language understanding model
Provision Azure resources for Azure AI Language resource
Define intents, utterances, and entities
Use patterns to differentiate similar utterances
Use pre-built entity components
Train, test, publish, and review an Azure AI Language model

Module 14: Create a custom text classification solution
Understand types of classification projects
Build a custom text classification project
Tag data, train, and deploy a model
Submit classification tasks from your own app

Module 15: Create a custom named entity extraction solution
Understand custom named entities and how they’re labeled.
Build a Language service project.
Label data, train, and deploy an entity extraction model.
Submit extraction tasks from your own app.

Module 16: Translate text with Azure AI Translator service
Provision a Translator resource
Understand language detection, translation, and transliteration
Specify translation options
Define custom translations

Module 17: Create speech-enabled apps with Azure AI services
Provision an Azure resource for the Azure AI Speech service
Use the Azure AI Speech to text API to implement speech recognition
Use the Text to speech API to implement speech synthesis
Configure audio format and voices
Use Speech Synthesis Markup Language (SSML)

Module 18: Translate speech with the Azure AI Speech service
Provision Azure resources for speech translation.
Generate text translation from speech.
Synthesize spoken translations.

Module 19: Create an Azure AI Search solution
Create an Azure AI Search solution
Develop a search application

Module 20: Create a custom skill for Azure AI Search
Implement a custom skill for Azure AI Search
Integrate a custom skill into an Azure AI Search skillset

Module 21: Create a knowledge store with Azure AI Search
Create a knowledge store from an Azure AI Search pipeline
View data in projections in a knowledge store

Module 22: Plan an Azure AI Document Intelligence solution
Describe the components of an Azure AI Document Intelligence solution.
Create and connect to Azure AI Document Intelligence resources in Azure.
Choose whether to use a prebuilt, custom, or composed model.

Module 23: Use prebuilt Form Recognizer models
Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
Analyze forms by using the General Document, Read, and Layout models.
Analyze forms by using financial, ID, and tax prebuilt models

Module 24: Extract data from forms with Form Recognizer
Identify how Azure Document Intelligence’s layout service, prebuilt models, and custom service can automate processes
Use Azure Document Intelligence’s Optical Character Recognition (OCR) capabilities with SDKs, REST API, and Azure Document Intelligence Studio
Develop and test custom models

Module 25: Get started with Azure OpenAI Service
Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio’s playgrounds.
Generate completions to prompts and begin to manage model parameters.

Module 26: Build natural language solutions with Azure OpenAI Service
Integrate Azure OpenAI into your application
Differentiate between different endpoints available to your application
Generate completions to prompts using the REST API and language specific SDKs

Module 27: Apply prompt engineering with Azure OpenAI Service
Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models’ performance.
Know how to design and optimize prompts to better utilize AI models.
Include clear instructions, request output composition, and use contextual content to improve the quality of the model’s responses.

Module 28: Generate code with Azure OpenAI Service
Use natural language prompts to write code
Build unit tests and understand complex code with AI models
Generate comments and documentation for existing code

Module 29: Generate images with Azure OpenAI Service
Describe the capabilities of DALL-E in the Azure openAI service
Use the DALL-E playground in Azure OpenAI Studio
Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps

Module 30: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
Describe the capabilities of Azure OpenAI on your data
Configure Azure OpenAI to use your own data
Use Azure OpenAI API to generate responses based on your own data

Module 31: Fundamentals of Responsible Generative AI
Describe an overall process for responsible generative AI solution development
Identify and prioritize potential harms relevant to a generative AI solution
Measure the presence of harms in a generative AI solution
Mitigate harms in a generative AI solution
Prepare to deploy and operate a generative AI solution responsibly

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