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MS-AI102T00: Designing and Implementing a Microsoft Azure AI Solution
Course Code: MS-AI102T00
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.
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C#, Python, or JavaScript and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
Before attending this course, delegates must have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of either C#, Python, or JavaScript
After completing this course, students will be able to:
- Create, configure, deploy, and secure Azure Cognitive Services
- Integrate speech services
- Integrate text analytics
- Create language understanding capabilities with LUIS
- Create and manage Azure Cognitive Search solutions
- Create intelligent agents using the Bot Framework
- Implement Computer Vision solutions
This course will prepare delegates to write the AI-102: Designing and Implementing an Azure AI Solution exam.
Modules
As an aspiring Azure AI Engineer, you should understand core concepts and principles of AI development, and the capabilities of Azure services used in AI solutions.
Lessons
-Introduction.
- 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 the Azure Bot Service.
- Understand capabilities of Azure Cognitive Search.
- Exercise - Utilize prompt engineering in your application.
-Knowledge check.
-Summary.
After completing this module, you'll be able to:
- 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 the Azure Bot Service.
- Understand capabilities of Azure Cognitive Search.
Azure AI Services enable developers to easily add AI capabilities into their applications. Learn how to create and consume these services.
Lessons
-Introduction
- Provision an Azure AI Services resource
- Identify endpoints and keys
- Use a REST API
- Use an SDK
- Exercise - Use Azure AI Services
-Knowledge check
-Summary
After completing this module, you will be able to:
- 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 to consume an Azure AI service.
Securing Azure AI Services can help prevent data loss and privacy violations for user data that may be a part of the solution.
Lessons
- Introduction.
- Consider authentication
- Implement network security.
- Exercise - Manage Azure AI Services Security.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Consider authentication for Azure AI Services
- Manage network security for Azure AI Services
Azure AI Services enable you to integrate artificial intelligence into your applications and services. It's important to be able to monitor Azure AI Services in order to track utilization, determine trends, and detect and troubleshoot issues.
Lessons
- Introduction.
- Monitor cost.
- Create alerts.
- View metrics.
- Manage diagnostic logging.
- Exercise - Monitor Azure AI Services.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Monitor Azure AI Services costs
- Create alerts
- View metrics
- Manage diagnostic logging
Learn about Container support in Azure AI Services allowing the use of APIs available in Azure and enable flexibility in where to deploy and host the services with Docker containers.
Lessons
- Introduction.
- Understand containers.
- Use Azure AI Services containers.
- Exercise - Use a container.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Create Containers for Reuse
- Deploy to a Container
- Secure a Container
- Consume Azure AI Services from a Container
With the Azure AI Vision service, you can use pre-trained models to analyse images and extract insights and information from them.
Lessons
- Introduction.
- Provision an Azure AI Vision resource.
- Analyse an image.
- Generate a smart-cropped thumbnail and remove background.
- Exercise - Analyse images with Azure AI Vision.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Provision an Azure AI Vision resource.
- Analyse an image.
- Generate a smart-cropped thumbnail.
Classify images by training a custom model with Azure AI Vision.
Lessons
- Introduction.
- Understand custom model types.
- Create a custom project.
- Label and train a custom model.
- Exercise - Classify images with Azure AI Custom Vision custom model.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Create a custom Azure AI Vision classification model
- Understand image classification
- Train an image classifier in Vision Studio
- Understand object detection
The ability for applications to detect human faces, analyse facial features and emotions, and identify individuals is a key artificial intelligence capability.
Lessons
- Introduction.
- 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.
- Exercise - Detect, analyse, and identify faces.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- 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.
Azure's AI Vision service uses algorithms to process images and return information. This module teaches you how to use the Image Analysis API for optical character recognition (OCR).
Lessons
- Introduction.
- Explore Azure AI Vision options for reading text.
- Use the Read API.
- Exercise - Read text in images.
- Knowledge Check.
- Summary.
After completing this module, you will be able to:
- 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.
Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.
Lessons
- Introduction.
- Understand Azure Video Indexer capabilities.
- Extract custom insights.
- Use Video Analyzer widgets and APIs.
- Exercise - Analyse video.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Describe Azure Video Indexer capabilities.
- Extract custom insights.
- Use Azure Video Indexer widgets and APIs.
The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.
Lessons
- Introduction.
- Provision an Azure AI Language resource.
- Detect language.
- Extract key phrases.
- Analyse sentiment.
- Extract entities.
- Extract linked entities.
- Exercise - Analyse text.
- Knowledge check.
- Summary.
After completing this module, you will be able to use the Azure AI Language service to:
- Detect language from text
- Analyse text sentiment
- Extract key phrases, entities, and linked entities
The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.
Lessons
- Introduction.
- Understand question answering.
- Compare question answering to Azure AI Language understanding.
- Create a knowledge base.
- Implement multi-turn conversation.
- Test and publish a knowledge base.
- Use a knowledge base.
- Improve question answering performance.
- Exercise - Create a question answering solution.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- 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.
The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.
Lessons
- Introduction.
- Understand prebuilt capabilities of the Azure AI Language service.
- Understand resources for building a conversational language understanding model.
- Define intents, utterances, and entities.
- Use patterns to differentiate similar utterances.
- Use pre-built entity components.
- Train, test, publish, and review a conversational language understanding model.
- Exercise - Build an Azure AI services conversational language understanding model.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- 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.
The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.
Lessons
- Introduction.
- Understand types of classification projects.
- Understand how to build text classification projects.
- Exercise - Classify text.
- Knowledge check.
- Summary.
After completing this module, you'll be able to:
- 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.
Build a custom entity recognition solution to extract entities from unstructured documents.
Lessons
- Introduction.
- Understand custom named entity recognition.
- Label your data.
- Train and evaluate your model.
- Exercise - Extract custom entities.
- Knowledge check.
- Summary.
After completing this module, you'll be able to:
- Understand tagging entities in extraction projects
- Understand how to build entity recognition projects
The Translator service enables you to create intelligent apps and services that can translate text between languages.
Lessons
- Introduction.
- Provision an Azure AI Translator resource.
- Understand language detection, translation, and transliteration.
- Specify translation options.
- Define custom translations.
- Exercise - Translate text with the Azure AI Translator service.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Provision a Translator resource.
- Understand language detection, translation, and transliteration.
- Specify translation options.
- Define custom translations.
The Azure AI Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.
Lessons
- Introduction.
- Provision an Azure resource for speech.
- Use the Azure AI Speech to Text API.
- Use the text to speech API.
- Configure audio format and voices.
- Use Speech Synthesis Markup Language.
- Exercise - Create a speech-enabled app.
- Knowledge check
- Summary.
After completing this module, you will be able to:
- 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).
Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language and returning translations of the transcription in one or more other languages.
Lessons
- Introduction.
- Provision an Azure resource for speech translation.
- Translate speech to text.
- Synthesize translations.
- Exercise - Translate speech.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Provision Azure resources for speech translation.
- Generate text translation from speech.
- Synthesize spoken translations.
Unlock the hidden insights in your data with Azure AI Search.
Lessons
- Introduction.
- Manage capacity.
- Understand search components.
- Understand the indexing process.
- Search an index.
- Apply filtering and sorting.
- Enhance the index.
- Exercise - Create a search solution.
- Knowledge check.
- Summary.
After completing this module, you'll be able to:
- Create an Azure AI Search solution.
- Develop a search application.
Use the power of artificial intelligence to enrich your data and find new insights.
Lessons
- Introduction.
- Create a custom skill.
- Add a custom skill to a skillset.
- Exercise - Implement a custom skill.
- Knowledge check.
- Summary.
After completing this module, you'll be able to:
- Implement a custom skill for Azure AI Search.
- Integrate a custom skill into an Azure AI Search skillset.
Persist the output from an Azure AI Search enrichment pipeline for independent analysis or downstream processing.
Lessons
- Introduction.
- Define projections.
- Define a knowledge store.
- Exercise - Create a knowledge store.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Create a knowledge store from an Azure AI Search pipeline.
- View data in projections in a knowledge store.
Azure AI Language gives you the power of Natural Language Processing (NLP) to automatically understand and analyse text. You can use that power to enhance your search solutions.
Lessons
- Introduction.
- Explore the available features of Azure AI Language.
- Enrich a search index in Azure AI Search with custom classes and Azure AI Language.
- Exercise: Enrich a search index in Azure AI Search with custom classes.
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Use Azure AI Language to enrich Azure AI Search indexes.
- Enrich an AI Search index with custom classes.
Use more advanced features of Azure AI Search to improve your existing search solutions. Learn how to change the ranking on documents, boost terms, and allow searching in multiple languages.
Lessons
- Introduction.
- Explore the available features of Azure AI Language
- Enrich a search index in Azure AI Search with custom classes and Azure AI Language
- Exercise: Enrich a search index in Azure AI Search with custom classes
- Knowledge check.
- Summary.
After completing this module, you will be able to:
- Improve the ranking of a document with term boosting
- Improve the relevance of results by adding scoring profiles
- Improve an index with analysers and tokenized terms
- Enhance an index to include multiple languages
- Improve search experience by ordering results by distance from a given reference point
Custom skills allow you to enhance datasets as they pass through the enrichment pipeline. Azure Machine Learning can build custom models for regression or classification to enrich your search indexes.
Lessons
- Introduction
- Understand how to use a custom Azure Machine Learning skillset
- Enrich a search index using an Azure Machine Learning model
- Exercise: Enrich a search index using Azure Machine Learning model
- Knowledge check
- Summary.
After completing this module, you will be able to:
- Understand how to use a custom Azure Machine Learning skillset.
- Use Azure Machine Learning to enrich Azure AI Search indexes.
Use Azure Data Factory to add data that resides inside or outside the Azure platform into your search indexes.
Lessons
- Introduction
- Index data from external data sources using Azure Data Factory
- Index any data using the Azure Al Search push API
- Exercise: Add to an index using the push API
- Knowledge check
- Summary.
After completing this module, you will be able to:
- Use Azure Data Factory to copy data into an Azure AI Search Index
- Use the Azure AI Search push API to add to an index from any external data source
Maintain the performance, cost, and reliability of your Azure AI Search solutions.
Lessons
- Introduction
- Manage security of an Azure Al Search solution
- Optimize performance of an Azure Al Search solution
- Manage costs of an Azure Al Search solution
- Improve reliability of an Azure Al Search solution
- Monitor an Azure Al Search solution
- Debug search issues using the Azure portal
- Exercise - Debug search issues
- Knowledge check
- Summary
After completing this module, you will be able to:
- Application development experience with C# or Python
- Experience of building a basic search solution with Azure AI Search
- Familiarity with Microsoft Azure
Learn how to perform L2 ranking with semantic ranker in Azure AI Search.
Lessons
- Introduction
- What is semantic ranking?
- Set up semantic ranking
- Exercise - Use semantic ranking on an index
- Knowledge check
- Summary
After completing this module, you will be able to:
- Describe semantic ranking
- Set up semantic ranking
- Perform semantic ranking on an index
Learn how to perform vector search and retrieval in Azure AI Search.
Lessons
- Introduction
- What is vector search?
- Prepare your search
- Understand embedding
- Exercise - Use the REST API to run vector search queries
- Knowledge check
- Summary
After completing this module, you will be able to:
- Describe vector search
- Describe embeddings
- Run vector search queries using the REST API
Learn how to use Azure AI Document Intelligence to build solutions that analyse forms and output data for storage or further processing.
Lessons
- Introduction.
- Understand AI Document Intelligence.
- Plan Azure AI Document Intelligence resources.
- Choose a model type.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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.
Learn what data you can analyse by choosing prebuilt Azure AI Document Intelligence models and how to deploy these models in a Document Intelligence solution.
Lessons
- Introduction.
- Understand prebuilt models.
- Use the General Document, Read, and Layout models.
- Use financial, ID, and tax models.
- Exercise - Analyse a document using Azure AI Document Intelligence.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
- Analyse forms by using the General Document, Read, and Layout models.
- Analyse forms by using financial, ID, and tax prebuilt models.
Document intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document intelligence cognitive service.
Lessons
- Introduction.
- What is Azure Document Intelligence?
- Get started with Azure Document Intelligence.
- Train custom models.
- Use Azure Document Intelligence models.
- Use the Azure Document Intelligence Studio.
- Exercise - Extract data from custom forms.
- Knowledge check.
After completing this module, you will be able to:
- Identify how 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
Learn how to assemble custom models into composed solutions that can analyse different types of your own documents.
Lessons
- Introduction
- Understand composed models
- Assemble composed models
- Exercise: Create a composed model
- Knowledge check
- Summary
After completing this module, you will be able to:
- Describe business problems that you would use custom models and composed models to solve
- Train a custom model to obtain data from forms with unusual structures
- Create a composed model that can analyse forms in multiple formats
Learn how to use an Azure Document Intelligence solution as a custom skill to enrich content in an Azure AI Search pipeline.
Lessons
- Introduction
- Understand Azure Al Search enrichment pipelines
- Build an Azure Al Document Intelligence custom skill
- Exercise: Build and deploy an Azure Al Document Intelligence custom skill
- Knowledge check
- Summary
After completing this module, you will be able to:
- Describe how a custom skill can enrich content passed through an Azure AI Search pipeline.
- Build a custom skill that calls an Azure Forms Analyzer solution to obtain data from forms.
This module provides engineers with the skills to begin building an Azure OpenAI Service solution.
Lessons
- Introduction.
- Access Azure OpenAI Service
- Use Azure AI Studio.
- Explore types of generative AI models
- Deploy generative AI models.
- Use prompts to get completions from models.
- Test models in Azure OpenAI Studio's playgrounds.
- Exercise - Get started with Azure OpenAI Service.
- Knowledge check
- Summary
After completing this module, you will be able to:
- 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.
This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.
Lessons
- Introduction.
- Integrate Azure OpenAI into your app.
- Use Azure OpenAI REST API.
- Use Azure OpenAI SDK.
- Exercise - Integrate Azure OpenAI into your app.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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.
Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.
Lessons
- Introduction.
- Understand prompt engineering.
- Write more effective prompts.
- Provide context to improve accuracy.
- Exercise - Utilize prompt engineering in your application.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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.
This module shows engineers how to use the Azure OpenAI Service to generate and improve code.
Lessons
- Introduction.
- Construct code from natural language.
- Complete code and assist the development process.
- Fix bugs and improve your code.
- Exercise: Generate and improve code with Azure OpenAI Service.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- Use natural language prompts to write code.
- Build unit tests and understand complex code with AI models.
- Generate comments and documentation for existing code.
Persist the output from an Azure Cognitive Search enrichment pipeline for independent analysis or downstream processing.
Lessons
- Introduction.
- What is DALL-E?
- Explore DALL-E in Azure OpenAI Studio.
- Use the Azure OpenAI REST API to consume DALL-E models.
- Exercise - Generate images with a DALL-E model.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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
Azure OpenAI on your data allows developers to use supported AI chat models that can reference specific sources of data to ground the response.
Lessons
- Introduction.
- Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Add your own data source.
- Chat with your model using your own data.
- Exercise: Add your data for RAG with Azure OpenAI Service.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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
Generative AI enables amazing creative solutions but must be implemented responsibly to minimize the risk of harmful content generation.
Lessons
- Introduction.
- Plan a responsible generative AI solution.
- Identify potential harms.
- Measure potential harms.
- Mitigate potential harms.
- Operate a responsible generative AI solution.
- Exercise - Explore content filters in Azure OpenAI.
- Knowledge check.
- Summary
After completing this module, you will be able to:
- 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.