AI-900 Lesson 5 – Core ML Concepts & Azure Machine Learning (AutoML, Designer & Deployment)

In this AI-900 Azure AI Fundamentals lesson, we connect the **theory of machine learning** with the **Azure Machine Learning platform** that you need to know for the exam.

You will learn:

✅ Core Machine Learning Concepts
- What **features** and **labels** are in supervised learning
- Why we split data into **training**, **validation** and **test** sets
- What **overfitting** and **underfitting** mean, and how to recognize them from model behavior

✅ Azure Machine Learning (Azure ML) Essentials
- What the **Azure ML workspace** is used for
- How Azure ML helps you **prepare data, train models, deploy models and monitor them** in one place

✅ Automated Machine Learning (AutoML)
- How AutoML automatically tries multiple algorithms and hyperparameters
- When to use AutoML to help non-experts quickly build strong models
- Typical AI-900 exam hints that point to AutoML

✅ Azure ML Designer & Model Management
- How the **Azure ML designer** lets you build ML pipelines with a **drag-and-drop** interface
- How the **model registry** helps you version and manage models
- How to **deploy** models as endpoints and **monitor** them in production

By the end of this lesson, you will be able to:
- Identify features and labels in exam-style questions
- Explain why training/validation/test splits matter
- Recognize overfitting and underfitting when they appear in scenarios
- Map requirements like “non-expert team”, “drag-and-drop ML”, or “deploy model as a REST API” to the right **Azure ML capability** (AutoML, Designer, model deployment, etc.)

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Keep watching the next lessons to explore **Azure AI services for vision, language, speech, search and generative AI**, and complete your preparation for the **Microsoft Azure AI Fundamentals (AI-900)** certification.

Видео AI-900 Lesson 5 – Core ML Concepts & Azure Machine Learning (AutoML, Designer & Deployment) канала Tech Skills Lab