Azure AI Engineer Associate (AI-102) Study Guide. In-Depth Certification Guide and Practice Renaldi Gondosubroto

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Spis treści książki

• Preface
  • Why I Wrote This Book
  • Who This Book Is For
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to AI Solutions on Microsoft Azure
  • Introduction to the AI Landscape
    • The Minimally Qualified Candidate for the AI-102 Exam
    • Where We Are Now with AI
    • Fundamental AI Concepts
      • Data science
      • Machine learning
      • Artificial intelligence
      • Deep learning
    • The Six Core Principles: Considerations for Developing AI Responsibly
    • Explainable AI Techniques
  • Tech Setup
    • Setting Up Your Microsoft Azure Account
    • Configuring Your Azure AI Environment
    • Configuring Your Local Development Environment
  • Gaining an Understanding of Azure AIs Capabilities
    • The Capabilities of Microsoft Azures AI Services
      • Machine learning
      • Computer vision
    • Consuming AI Services
      • Microsoft Azure user interface
      • SDKs
      • REST APIs
    • Authentication and Security
      • Microsoft Entra ID (formerly known as Azure AD)
      • Subscription keys
      • Managed identities
      • More security measures
    • Billing and Cost Management
  • Your Responsibilities as an AI Engineer
    • Meeting Challenges and Managing Risks
      • Technical and scalability challenges
      • Security and compliance
      • Resource availability
      • Ethical and legal issues
    • Continuous Learning and Collaboration
    • User-Centered Design
  • Practical: Running a Text Analytics Service
  • Chapter Review
  • Chapter Quiz
• 2. Planning and Managing AI Solutions in Microsoft Azure
  • The Azure AI Project Lifecycle
    • Requirements definition and design
    • Development
    • Deployment
    • Integration
    • Maintenance
    • Performance Tuning
    • Monitoring
  • Practical: Designing an AI Solution
    • A Simple Example of Solution Design
    • Weighing the Trade-offs
  • Planning and Configuring Access and Security
    • Implementing the Appropriate Access Control Requirements
      • Data protection
      • RBAC on Azure
      • Managing account keys
      • Working with Azure Key Vault
      • Managing private communications
    • Working with Security over the Network
      • Virtual networks and subnets
      • Network security groups
      • Azure Application Gateway and Web Application Firewall
      • Azure Firewall
      • Networking for individual AI service instances
  • Creating and Managing Azure AI Services
    • Deploying an Azure AI Services Resource
    • Cost Optimization Strategies for Azure AI Services
    • Implementing a Container Deployment
    • Working with APIs and SDKs in Azure
      • Standard web service API primary operations
      • Relevant API documentation
      • Construction of the HTTP request
      • Sending requests
      • Handling responses
      • Handling errors
    • Practical: Designing an AI Solution with the REST API
  • Monitoring Azure AI Services
    • Proactively Monitoring Costs
      • Microsoft Cost Management
      • Azure Advisor
      • Cost Analysis
    • Using Metrics and Alerts
      • Alerts
      • Configuring diagnostic logging
      • Performance metrics
      • Error metrics
      • Model-specific metrics
    • Practical: Designing Your AI Solution
      • Setting up the key vault
      • Coding the system
      • Monitoring and logging your solution
  • Chapter Review
  • Chapter Quiz
• 3. Storing, Interpreting, and Visualizing Data
  • Data Storage and Management in Azure AI
    • Choosing Storage Options for AI Solutions
      • Understanding data types and requirements
        • Structured data
        • Unstructured data
      • Performance and scalability
      • Security and compliance
      • Ease of integration
      • Cost
      • Azure storage options
        • Azure Disk Storage
        • Azure Blob Storage
        • Azure Data Lake Storage
        • Azure Files
        • Azure NetApp Files
        • Azure File Sync
        • Azure Stack Edge
        • Azure Data Box
    • Data Management Best Practices
      • Data governance and cataloging
        • Step-by-step guide to cataloging AI data with Microsoft Purview
      • Data quality and preparation
        • Using Azure Data Factory for data preparation
        • Using Azure Databricks for data preparation
      • Data backup and disaster recovery
        • Data backup with Azure Backup
        • Disaster recovery with Azure Site Recovery
        • Implementing and managing backup and recovery strategies
        • Testing and validation
  • Data Interpretation for AI Solutions
    • Leveraging Azure AI for Data Analysis
      • Creating event-driven architectures
      • Building a unified solution
    • Model Training and Selection in Azure AI
      • Automated machine learning in Azure Machine Learning
      • Evaluation metrics and tools
        • Evaluation metrics
        • Tools in Azure Machine Learning
      • CI/CD for reproducibility
  • Data Visualization Techniques and Real-Time Analytics
    • Introduction to Azure Data Visualization Tools
      • Azure Machine Learning Studio
      • Power BI
      • Azure Data Explorer
      • Additional integrations
    • Real-Time Analytics and Decision Making
      • Real-time analytics
      • Near-real-time analytics
      • Non-real-time analytics
      • Decision framework
  • Implementing AI in Data Analysis
    • Integrating AI with Azures Data Platforms
      • Unified data architecture
      • Interoperability and integration
      • Security and compliance
    • Case Study of Data Analysis in Action
  • Practical: Building an AI-Powered Analytics Dashboard
    • Setting Up Azure Blob Storage
    • Uploading the Customer Feedback Data
    • Creating an Azure AI Services Language Service
    • Creating and Configuring an Azure SQL Database
    • Setting Up Azure Data Factory
    • Creating a Pipeline for Data Movement and Transformation
    • Creating a SQL Database to Store the Data
    • Visualizing the Data with Power BI
  • Chapter Review
  • Chapter Quiz
• 4. Building Decision Support Solutions with Azure AI
  • Introduction to Decision Support for Azure AI
    • Understanding Decision Support Systems
    • What Does Decision Support Look Like in the Azure AI Landscape?
  • Utilizing Azure AI Metrics Advisor to Implement Data Monitoring Solutions
    • Understanding Azure AI Metrics Advisor
      • Steps to follow when using Metrics Advisor
      • Features
      • When to use it
    • Implementing Data Monitoring Solutions
      • Creating the Azure AI Metrics Advisor resource
      • Connecting data sources
      • Ingesting and preprocessing data
      • Configuring anomaly detection
      • Setting up notifications and alerts
      • Monitoring and diagnostics
      • Continuous improvement
      • Integrating the API
      • Fine-tuning anomaly detection
      • Managing alerts and notifications
      • Configuring notification hooks
      • Querying anomalies and alerts
      • Customizing and extending the monitoring solution
  • Text Classification and Moderation with Azure AI Content Safety
    • Understanding Azure AI Content Safety
      • Different types of analysis made possible by Azure AI Content Safety
      • Performance optimization and moderation accuracy
      • Key features
      • When to use it
      • Security
    • Moderating Text with Azure AI Content Safety
      • The importance of text moderation
      • Steps involved in implementing text moderation
    • Moderating Images with Azure AI Content Safety
      • The importance of image moderation
      • Steps involved in implementing image moderation
    • Detecting Jailbreak Risks
    • Detecting Protected Material
    • Content Filtering for Text Moderation
    • Practical: Implementing a Text Moderation Solution with Azure AI Content Safety
      • Setting up the Azure AI Content Safety resource
      • Configuring environment variables
      • Installing the Azure AI Content Safety Python package
      • Extending the handling of the content
  • Chapter Review
  • Chapter Quiz
• 5. Implementing Computer Vision Solutions with Azure AI
  • Introduction to Azure AI Vision
    • What Is Computer Vision?
    • What Is Azure AI Vision?
    • Case Study
  • Image Analysis with Azure AI Vision
    • The Fundamentals of Image Analysis
    • Performing Image Analysis
      • Selecting the appropriate visual features
      • Detecting objects in images and generating image tags
      • Interpreting image processing responses
    • Extracting Text with Azure AI Vision
      • Choosing the workload
      • Practical: Extracting and converting handwritten text
  • Facial Recognition and Analysis
    • Fundamentals of Facial Recognition
      • Considerations
      • Functionalities
      • Response from performing the analysis
    • Practical: Implementing Facial Recognition in Your Application
  • Custom Vision and Object Detection
    • Building Custom Image Classification Models
      • Data collection and preparation
      • Choosing the appropriate model
      • Accuracy trade-offs and decision frameworks
      • Performance benchmarks for different types of hardware
      • Model optimization techniques
      • Labeling images
    • Implementing Object Detection
      • Training the custom image model
      • Evaluating the custom vision model metrics
      • Publishing and consuming a custom vision model
    • Practical: Creating a Custom Vision Object Detection Solution
  • Working with Video Content
    • Using Azure AI Video Indexer
    • Practical: Analyzing Video Content with Azure AI Video Indexer
  • Chapter Review
  • Chapter Quiz
• 6. Implementing Natural Language Processing Solutions
  • Fundamentals of Natural Language Processing
    • Introduction to NLP
    • Core Components of NLP
      • Transformer architectures and attention mechanisms
      • Pretrained models
      • Basic NLP code example
    • Common NLP Techniques and Algorithms
      • Deeper implementation details and optimization
      • Handling different languages and writing systems
      • Failure modes and edge cases
      • Large-scale deployment and real-time processing
    • A Look into NLP in Microsoft Azure
  • Introduction to the Azure AI Language Service
    • Understanding Azure AI Language
    • Using Prebuilt Solutions
      • Extracting key phrases from text
      • Extracting entities from text
      • Performing sentiment analysis
      • Detecting the language used in text
      • Detecting personally identifiable information
    • Using Azure AI Speech to Process Speech
      • Understanding Azure AI Speech
      • Implementing Prebuilt Speech Solutions
        • Implementing speech-to-text solutions
        • Implementing text-to-speech solutions
        • Implementing intent recognition
        • Implementing keyword recognition
      • Implementing Custom Speech Solutions
  • Translating with Azure AI Translator
    • Understanding Azure AI Translator
      • Technical details on neural machine translation and formatting preservation
      • Optimizing batch translation
    • Implementing Prebuilt Translation Solutions
      • Translating text and documents
      • Translating speech to text and speech to speech
    • Implementing Custom Translation Solutions
    • Practical: Building a Custom Translation Solution
  • More Best Practices
  • Chapter Review
  • Chapter Quiz
• 7. Advanced NLP Techniques and Language Understanding
  • Working with Language Understanding Models
    • Creating Intents, Utterances, and Entities
    • Building Language Understanding Models
      • Training the model to recognize intents and entities
      • Evaluating model performance with labeled datasets
      • Handling ambiguous user input and improving accuracy
      • Implementing your trained model on a publicly accessible endpoint
      • Analyzing predictions and refining the model
    • Optimizing Language Understanding Models
    • Backing Up and Recovering Language Understanding Models
    • Practical: Building and Integrating Your Own Language Understanding Model
      • Step 1: Create an Azure AI Language resource
      • Step 2: Set up Language Studio
      • Step 3: Define intents, utterances, and entities
      • Step 4: Train your model
      • Step 5: Evaluate and improve your model
      • Step 6: Deploy your model
      • Step 7: Integrate the model into your application
      • Step 8: Clean up your resources
  • Building Question-Answering Solutions
    • Understanding Question-Answering Solutions
      • Key features of question-answering solutions
      • Fundamentals of question answering
        • Components of a QA system
        • Types of QA systems
        • Real-world use cases
      • Monitoring usage and identifying areas for improvement
    • Practical: Building Your Own Question-Answering Solution
      • Step 1: Build the question-answering knowledge base
      • Step 2: Train and test your model
      • Step 3: Deploy your model
      • Step 4: Integrate the model into your application
      • Step 5: Integrate with Azure AI Bot Service (optional)
      • Step 6: Clean up your resources
    • Advanced Capabilities
      • Adding multiturn conversations
      • Practical: Implementing multiturn conversations
        • Step 1: Create a question-answering project
        • Step 2: Add new question-and-answer pairs
        • Step 3: Test multiturn conversations
        • Step 4: Deploy your project
        • Step 5: Integrate with applications
      • Example JSON requests and responses
      • Alternate phrasing
      • Practical: Alternate Phrasing
        • Step 1: Access your project in Language Studio
        • Step 2: Add alternate phrasing to a Q&A pair
        • Step 3: Add synonyms and colloquial terms
        • Step 4: Test the Q&A pair
        • Step 5: Add alternate phrasing to a Q&A pair
      • Adding chit-chat
        • Step 1: Add chit-chat to your sources
        • Step 2: Edit chit-chat Q&A pairs
        • Step 3: Add custom chit-chat Q&A pairs
        • Step 4: Test chit-chat integration
        • Step 5: Deploy your updated knowledge base
        • Meeting user expectations and avoiding bias
        • Best practices for chit-chat integration
      • Exporting a knowledge base
  • Chapter Review
  • Chapter Quiz
• 8. Implementing Knowledge Mining and Document Intelligence Solutions
  • Planning and Implementing a Knowledge-Mining Solution with Azure AI Search
    • Understanding Azure AI Search
      • Components of search
      • The indexing process
    • Creating a Search Service
      • Creating data sources
      • Implementing custom skills
    • Optimizing Search Performance
      • Creating an index
      • Running an indexer
      • Querying an index
      • Working with custom classes
    • Maintaining a Search Solution
      • Security
      • Performance
      • Managing costs
      • Debugging issues
    • Advanced Search Features
      • Using term boosting
      • Scoring profiles
      • Analyzers and tokenized terms
      • Using multiple languages
      • Using semantic search features
      • Using vector search
  • Working with Document Intelligence Solutions in Azure AI Document Intelligence
    • Understanding Azure AI Document Intelligence
      • Practical: Setting up a Document Intelligence resource
      • Types of models
      • Using a custom model
      • Practical: Using prebuilt models
        • Integrating with Azure AI Search
        • Indexing data from Azure Blob Storage
    • Practical: Implementing a Custom Document Intelligence Model
      • Step 1: Prepare your training data
      • Step 2: Upload your training data to Azure Blob Storage
      • Step 3: Train your custom model
      • Step 4: Test your custom model
      • Step 5. Use the custom model in your application
    • Practical: Creating a Composed Document Intelligence Model
      • Step 1: Train separate models for each document type
      • Step 2: Create a composed model
      • Step 3. Deploy and use your composed model
    • Practical: Building Custom Skills for Azure AI Search
      • Defining a custom skill in Azure AI Search
      • Implementing a custom function
      • Creating an index and an indexer
      • Running the indexer
      • Querying the processed content
  • Chapter Review
  • Chapter Quiz
• 9. Utilizing the Azure OpenAI Service for Generative AI Applications
  • Generative AI on Microsoft Azure
    • Types of Generative AI Models
      • OpenAI o-series models
        • Capabilities
        • Use cases
      • GPT-4 models
        • Capabilities
        • Use cases
      • Embedding models
        • Capabilities
        • Use cases
      • DALL-E models
        • Capabilities
        • Use cases
      • Choosing a model
      • Limitations and ethical considerations
    • Building Responsible Generative AI Solutions
      • Identifying and mitigating potential harms
      • Measuring potential harms
      • Mitigating potential harms
      • Working with responsible generative AI solutions
  • Prompt Engineering with the Azure OpenAI Service
    • Writing Effective Prompts
    • Advanced Techniques and Best Practices
  • Generating Content with the Azure OpenAI Service
    • Using the Azure OpenAI Service
      • The completions playground
        • Temperature
        • Max length (in tokens)
        • Stop sequences
        • Top P (aka nucleus sampling)
        • Frequency penalty
        • Presence penalty
        • Pre-response text
        • Post-response text
      • The chat playground
    • Using Azure OpenAI in Your Applications
    • Generating Text
      • Practical: Using Azure OpenAI API GPT-4 to generate text
        • Step 1: Set up an Azure OpenAI Service resource
        • Step 2: Set up your development environment
        • Step 3: Implement the GPT-4 text generation script
        • Step 4: Run the script
      • Best practices
    • Generating Images
      • Practical: Generating images with the Azure OpenAI DALL-E API
        • Step 1: Deploy the DALL-E model
        • Step 2: Update environment variables
        • Step 3: Implement a DALL-E image generation script
        • Step 4: Run the script
      • Best practices
    • Generating Code
      • Fixing a bug in a piece of code
      • Writing unit tests
      • Building complete functions and classes
      • Automated code documentation
      • Practical: Creating a chatbot
        • Step 1: Deploy the GPT-3.5 Turbo model
        • Step 2: Install the required Python packages
        • Step 3: Update the environment variables
        • Step 4: Implement the chatbot script
        • Step 5: Run the chatbot
      • Best practices
  • Fine-Tuning and Optimizing Generative AI Models
    • Fine-Tuning Your OpenAI Model
    • Integrating Data Sources
    • Interacting with the Model
  • Retrieval-Augmented Generation
    • Understanding RAG
      • Real-life use cases of RAG
      • Using RAG in Azure OpenAI
      • Performance optimization strategies for RAG
    • Practical: Implementing RAG with Azure OpenAI and Azure AI Search
      • Step 1: Create an Azure AI Search resource
      • Step 2: Deploy models in Azure OpenAI
      • Step 3: Set up an Azure AI Search index
      • Step 4: Configure environment variables
      • Step 5: Implement the RAG solution
      • Step 6: Run the script
      • Step 7: Test and refine the application
      • Best practices
  • Chapter Review
  • Chapter Quiz
• 10. The Future of AI in Microsoft Azure
  • A Look at Key Trends
    • Advances in Complex Reasoning
      • What makes these models different?
      • Azures playground for reasoning engines
        • Why this matters now
      • Small Language Models
        • Technologies and tools in Azure
        • Integration of SLMs with Azure Services
        • Building with SLMs in Azure
        • Implications for Azure
      • Multimodal AI
        • Technologies and tools in Azure
        • Applications and implications
      • Agentic AI
      • The Integration of Azure AI with the Entire Azure Platform
        • AI-driven analytics and insights
        • AI-enhanced security
        • Integrating AI with the development environment
        • Holistic AI architecture
        • A comprehensive ecosystem
      • Practical: Creating a Complex Architecture on Azure with AI Services
        • Step 1: Deploy Azure Storage for data ingestion
        • Step 2: Configure Azure AI Services for data processing
        • Step 3: Set up Azure AI Search
        • Step 4: Deploy Azure Machine Learning
        • Step 5: Integrate the Azure OpenAI Service
        • Step 6: Build and deploy AI models
        • Step 7: Integrate and secure the architecture
        • Step 8: Create and deploy a web application for user interaction
    • Streamlining the AI Development Process
    • Developing with Microsoft Copilot
      • Streamlining the development process with Azure OpenAI and the Teams AI library
      • Enhancing productivity with AI and Copilot in Microsoft Power Platform
    • Practical: Using Copilot in the Azure Ecosystem (with Code)
      • Step 1: Install and launch Azure Data Studio
      • Step 2: Set up GitHub Copilot
      • Step 3: Sign in to GitHub
      • Step 4: Use GitHub Copilot in Azure Data Studio to write SQL queries with Copilot assistance
    • Working with Prompt Flow in Azure AI Foundry
      • Phase 1: Initialization
      • Phase 2: Experimentation
      • Phase 3: Evaluation
      • Phase 4: Refinement
      • Phase 5: Production
  • Microsoft Fabric
    • Building AI Applications with Microsoft Fabric
    • Fostering a Data Culture Across Organizations
      • Ways to support the adoption of a data culture
      • Obstacles and solutions for the development of a data culture
    • Practical: Creating an End-to-End Solution with Microsoft Fabric
      • Step 1: Set up your environment
      • Step 2: Create a lakehouse
      • Step 3: Ingest data
      • Step 4: Transform data
      • Step 5: Create reports
      • Step 6: Scheduling and orchestration (optional)
      • Step 7: Clean up
  • A Closing Note
  • Chapter Quiz
• A. Answer Keys
  • Chapter 1 Answer Key
  • Chapter 2 Answer Key
  • Chapter 3 Answer Key
  • Chapter 4 Answer Key
  • Chapter 5 Answer Key
  • Chapter 6 Answer Key
  • Chapter 7 Answer Key
  • Chapter 8 Answer Key
  • Chapter 9 Answer Key
  • Chapter 10 Answer Key
• Index

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