Microsoft Azure AI Fundamentals AI-900 Exam Guide Gain Proficiency in Azure AI and Machine Learning Concepts and Services to Excel in the AI-900 Exam
Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP,...
| Autor principal: | |
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
2024.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009826140306719 |
Tabla de Contenidos:
- Cover
- Title page
- Copyright and Credits
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1: Identify Features of Common AI Workloads
- Chapter 1: Identify Features of Common AI Workloads
- Making the Most Out of this Book
- Your Certification and Beyond
- Identify features of data monitoring and anomaly detection workloads
- Identify features of content moderation and personalization workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document intelligence workloads
- Summary
- Exam Readiness Drill
- Working On Timing
- Chapter 2: Identify the Guiding Principles for Responsible AI
- Understanding ethical principles
- Describe considerations for accountability
- Describe considerations for inclusiveness
- Describe considerations for reliability and safety
- Understand explainable principles
- Describe considerations for fairness
- Describe considerations for transparency
- Describe considerations for privacy and security
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Exam Readiness Drill
- Working On Timing
- Part 2: Describe the Fundamental Principles of Machine Learning on Azure
- Chapter 3: Identify Common Machine Learning Techniques
- Understanding machine learning terminology
- Training
- Inferencing
- Identify regression machine learning scenarios
- Example
- Evaluation metrics
- Applications
- Identify classification machine learning scenarios
- Binary classification
- Multiclass classification
- Identify clustering machine learning scenarios
- Example
- Evaluation metrics
- Applications
- Identify features of deep learning techniques
- Example
- Applications
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Exam Readiness Drill
- Working On Timing
- Chapter 4: Describe Core Machine Learning Concepts
- Identify features and labels in a dataset for machine learning
- Identifying features in a dataset
- Identifying labels in a dataset
- Describe how training and validation datasets are used in machine learning
- Training set
- Validation set
- Summary
- Exam Readiness Drill
- Chapter Review Questions
- Exam Readiness Drill
- Working On Timing
- Chapter 5: Describe Azure Machine Learning Capabilities
- What is Azure ML?
- Describe capabilities of AutoML
- AutoML use cases
- Training, validation, and test scenarios
- Feature engineering
- Ensemble models
- Describe data and compute services for data science and machine learning
- Compute
- Data
- Datastore
- Environments
- Model
- Workspaces
- Subscription
- Storage account
- Key Vault
- Application Insights
- Container Registry
- Describe model management and deployment capabilities in Azure ML
- Model management and deployment capabilities
- MLOps
- Build a machine learning model in Azure ML
- Creating a machine learning workspace
