Azure ai engineer associate

Module 1: Azure AI Fundamentals

1.1 Core AI Concepts

Okay, let’s break down the “Core AI Concepts” as they relate to the Azure AI Engineer Associate exam, without any fluff. Think of these as the fundamental building blocks you need to understand before you can build anything complex with Azure AI.

1. Machine Learning (ML)

• Definition: Machine learning is the practice of teaching computers to learn from data without being explicitly programmed. Instead of writing specific rules, you feed a machine learning algorithm data, and it learns patterns and makes predictions based on those patterns.
• Example: Training a model on historical customer purchase data to predict which customers are most likely to churn (cancel their subscription). The model identifies patterns in the data (e.g., frequency of purchases, types of products bought, customer service interactions) that are correlated with churn.

2. Natural Language Processing (NLP)

• Definition: NLP is a field of AI that focuses on enabling computers to understand, interpret, and generate human language. It allows machines to interact with humans in a more natural way.
• Example: Using Azure Cognitive Services to analyze customer reviews of a product and determine the overall sentiment (positive, negative, neutral). The NLP model identifies keywords and phrases that indicate positive or negative opinions.

3. Computer Vision

• Definition: Computer vision is a field of AI that enables computers to “see” and interpret images or videos. It allows machines to identify objects, people, and scenes within visual data.
• Example: Using Azure’s Custom Vision service to train a model to identify different types of defects on a manufactured product based on images from a production line camera. The model learns to distinguish between scratches, dents, and other imperfections.

4. Deep Learning (DL)

• Definition: Deep learning is a subfield of machine learning that uses artificial neural networks with multiple layers (hence “deep”) to analyze data. These networks can automatically learn complex features from data, making them particularly powerful for tasks like image recognition and NLP.
• Example: Using a deep learning model (like a convolutional neural network) in Azure Machine Learning to classify images of different types of flowers. The model automatically learns features like petal shape, color patterns, and textures to differentiate between the flower types.

5. Responsible AI

• Definition: Responsible AI encompasses the ethical considerations and best practices for developing and deploying AI systems in a way that is fair, reliable and safe, private and secure, inclusive, transparent, and accountable.
• Example: Using Azure Machine Learning’s fairness assessment tools to evaluate whether a loan approval model is unfairly biased against a particular demographic group. This helps ensure that the AI system is making fair and equitable decisions.

In summary, to understand Core AI Concepts, you need to know the basic principles behind machine learning, natural language processing, computer vision, deep learning, and the importance of responsible AI practices. Be prepared to apply these concepts to practical scenarios within the Azure ecosystem.

1.2 Azure AI Services Overview

1.3 Responsible AI Principles

Module 2: Hands-on Training for Azure Cognitive Services

2.1 Computer Vision API Implementation

2.2 Natural Language Processing (NLP) with Azure Cognitive Services

2.3 Speech Services Implementation and Customization

2.4 Decision API Implementation (Content Moderator, Anomaly Detector)

2.5 Azure Cognitive Search Integration

Module 3: Machine Learning

3.1 Azure Machine Learning Workspace Setup and Configuration

3.2 Data Preparation and Feature Engineering in Azure ML

3.3 Model Training and Evaluation in Azure ML

3.4 Automated Machine Learning (AutoML) in Azure ML

3.5 Hyperparameter Tuning and Optimization

3.6 Model Deployment and Monitoring

Module 4: AI

4.1 Implementing Custom Vision Solutions

4.2 Creating and Deploying Custom Speech Models

4.3 Building and Integrating Language Understanding (LUIS) Applications

4.4 Developing Intelligent Bots with Azure Bot Service

Module 5: Developing AI Solutions

5.1 Designing AI Solutions for Specific Business Problems

5.2 Integrating AI Services with Existing Applications

5.3 Building Scalable and Reliable AI Pipelines

Module 6: Monitoring and Troubleshooting AI Solutions

6.1 Monitoring Model Performance and Accuracy

6.2 Troubleshooting Deployment Issues

6.3 Managing AI Infrastructure Costs

Module 7: AI Security and Compliance

7.1 Securing AI Models and Data

7.2 Ensuring Compliance with Data Privacy Regulations

7.3 Implementing Responsible AI Practices

Module 8: Working with AI Services

8.1 Using the Azure AI CLI

8.2 Using AI SDKs

8.3 Managing AI Resources in Azure