Mastering Microsoft Azure AI-900: Core Concepts and Tools for AI Solutions

Duration: Hours

Enquiry


    Category: Tag:

    Training Mode: Online

    Description

    Introduction of Microsoft Azure AI-900

    The Microsoft Azure AI-900 exam focuses on validating your knowledge of artificial intelligence concepts and services within Microsoft Azure. This course is designed to help you master the fundamental concepts of AI, machine learning, and related Azure services, providing you with the skills needed to pass the Azure AI-900 certification exam. Whether you’re an aspiring AI engineer or a developer looking to enhance your understanding of Azure’s AI capabilities, this course will guide you through the essential tools and concepts for building AI solutions on the Azure platform.

    Prerequisites

    • Basic understanding of cloud computing
    • Familiarity with general programming concepts (helpful but not required)
    • Interest in AI and machine learning

    Table of Contents

    1. Introduction to Azure AI and Machine Learning
      1.1 What is Artificial Intelligence (AI)?
      1.2 Overview of AI in Azure(Ref: Mastering Grafana & Prometheus: Real-Time Monitoring and Visualization)
      1.3 Understanding Cloud AI Solutions
      1.4 Introduction to Machine Learning (ML) and Deep Learning
      1.5 Azure AI-900 Exam Overview and Preparation
    2. Core AI Concepts
      2.1 What is Machine Learning?
      2.2 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
      2.3 AI Model Lifecycle: Training, Testing, and Deployment
      2.4 Overview of Common AI Algorithms
      2.5 Ethics in AI: Fairness, Accountability, and Transparency
    3. Azure Cognitive Services Overview
      3.1 Introduction to Cognitive Services
      3.2 Vision Services: Face API, Computer Vision, Custom Vision
      3.3 Speech Services: Speech Recognition, Speech Synthesis, Speech Translation
      3.4 Language Services: Language Understanding (LUIS), Text Analytics, Translator
      3.5 Decision Services: Personalizer, Anomaly Detector, Content Moderator
      3.6 Integrating Cognitive Services into Applications
    4. Azure Machine Learning Service (Azure ML)
      4.1 Introduction to Azure Machine Learning
      4.2 Creating and Managing Workspaces in Azure ML
      4.3 Building, Training, and Deploying ML Models with Azure ML Studio
      4.4 Automating Machine Learning (AutoML) in Azure
      4.5 Azure ML SDK: Interacting with Azure ML from Code
      4.6 Managing Models and Pipelines in Azure ML
    5. Azure Bot Services and AI Solutions
      5.1 Introduction to Azure Bot Services
      5.2 Building Chatbots with Azure Bot Framework
      5.3 Integrating AI in Chatbots with Language Understanding (LUIS)
      5.4 Deploying and Managing Bots on Azure
      5.5 Real-World Bot Applications in Customer Service and Support
    6. Data and AI Solutions on Azure
      6.1 Preparing Data for AI in Azure: Data Sources and Datasets
      6.2 Working with Azure Data Services: Azure Data Lake, Azure SQL Database
      6.3 Using Azure Databricks for Big Data and Machine Learning
      6.4 Data Integration in Azure AI Solutions: Data Factory and Logic Apps
      6.5 Data Governance and Security for AI Solutions
    7. AI and ML Integration in Azure for Business Solutions
      7.1 AI Solutions for Healthcare, Finance, Retail, and Manufacturing
      7.2 Integrating AI into Existing Business Applications
      7.3 Enhancing Business Insights with AI and Data Analytics
      7.4 Building Scalable AI Solutions on Azure
      7.5 AI for Decision Making: Insights and Predictive Analytics
    8. AI Deployment and Monitoring in Azure
      8.1 Deploying AI Models to Azure Kubernetes Service (AKS)
      8.2 Real-Time Inference with Azure ML
      8.3 Monitoring AI Models and Pipelines
      8.4 Continuous Integration and Continuous Deployment (CI/CD) for AI Models
      8.5 Best Practices for Managing AI Solutions in Production
    9. Exam Preparation and Practice Tests
      9.1 Reviewing Key Concepts for AI-900 Exam
      9.2 Practice Test 1: Core AI Concepts and Tools
      9.3 Practice Test 2: Azure Cognitive Services and Machine Learning
      9.4 Practice Test 3: AI Solutions and Business Applications
      9.5 Tips and Strategies for Passing the AI-900 Exam
    10. Conclusion and Certification Path
      10.1 Recap of Core Concepts
      10.2 Final Review: Key Takeaways from the Course
      10.3 Preparing for the Future: Advanced Azure AI Certifications
      10.4 Career Path and Opportunities in AI and Azure
      10.5 Certification Exam Information

    Conclusion

    Upon completing this course, you will have a thorough understanding of core AI concepts and the Azure tools used to create intelligent solutions. You’ll be equipped with the knowledge to confidently tackle the AI-900 certification exam and apply Azure’s AI and machine learning services to real-world business problems. Whether you’re looking to enhance your career in AI or build advanced cloud-based AI applications, this course will serve as a strong foundation for your learning journey.

    Reference

    Reviews

    There are no reviews yet.

    Be the first to review “Mastering Microsoft Azure AI-900: Core Concepts and Tools for AI Solutions”

    Your email address will not be published. Required fields are marked *

    Enquiry


      Category: Tag: