AI-900 Labs
To efficiently prepare for the exam through hands-on learning, we can create small projects or practical exercises for each of the main areas covered by the AI-900 exam.
- Describe Artificial Intelligence Workloads and Considerations
- Classify AI vs. Non-AI Solutions
- Describe Fundamental Principles of Machine Learning on Azure
- Build a Basic Predictive Model using Azure Machine Learning
- Describe Features of Computer Vision Workloads on Azure
- Image Classification using Azure Custom Vision
- Describe Features of Natural Language Processing (NLP) Workloads on Azure
- Sentiment Analysis with Azure Text Analytics
- Describe Features of Conversational AI Workloads on Azure
- Build a Simple FAQ Bot using Azure Bot Service and QnA Maker
1. Describe Artificial Intelligence Workloads and Considerations
Project: Classify AI vs. Non-AI Solutions
Objective: To understand the types of AI workloads and identify scenarios where AI can be used effectively.
Steps: 1. Research AI Use Cases: Find examples of AI applications in various industries (e.g., healthcare, finance, retail, etc.). Write a brief description of each use case. 2. Identify AI Components: For each use case, identify the AI components involved (e.g., machine learning, computer vision, natural language processing). 3. Categorize Workloads: Classify the workloads based on the AI solutions: - Knowledge mining - Anomaly detection - Prediction and forecasting - Natural language processing - Computer vision 4. Non-AI vs. AI Solution: Provide a real-world example of a problem that could be solved with a traditional non-AI approach versus an AI approach (e.g., rule-based vs. machine learning for spam detection). 5. Ethical Considerations: List potential ethical implications or considerations for each AI use case, such as bias, privacy, or security concerns.
Tools: Pen and paper, or a document editor like Microsoft Word or Google Docs.
Outcome: Develop a deeper understanding of when and how to apply AI technologies and the ethical considerations associated with AI solutions.
2. Describe Fundamental Principles of Machine Learning on Azure
Project: Build a Basic Predictive Model using Azure Machine Learning
Objective: To create a basic machine learning model to understand the process of data ingestion, model training, and evaluation on Azure.
Steps: 1. Set Up Azure Machine Learning Workspace: - Create an Azure Machine Learning workspace. - Set up the necessary environment, including compute resources.
- Data Ingestion:
- Use a sample dataset from Azure Open Datasets (e.g., NYC taxi fares dataset).
-
Explore and clean the data using Azure Machine Learning Designer or Python SDK in Jupyter Notebook.
-
Model Training:
- Use the Azure Machine Learning Designer (drag-and-drop interface) to build a simple regression model to predict taxi fares.
- Split the data into training and testing datasets.
-
Train the model using a linear regression algorithm.
-
Evaluate the Model:
- Evaluate model performance using metrics such as Mean Absolute Error (MAE) and R-squared.
-
Tune the model by adjusting parameters to improve accuracy.
-
Deploy the Model:
- Deploy the trained model as a web service on Azure.
- Test the web service by sending a sample input and receiving the predicted output.
Tools: Azure Machine Learning, Azure Machine Learning Designer, Jupyter Notebook.
Outcome: Gain practical experience in setting up an Azure ML environment, training a basic model, and deploying it as a web service.
3. Describe Features of Computer Vision Workloads on Azure
Project: Image Classification using Azure Custom Vision
Objective: To build, train, and test a custom image classification model using Azure's Custom Vision service.
Steps: 1. Set Up Custom Vision Service: - Create a Custom Vision project in the Azure portal. - Choose a classification project type (e.g., multi-class classification).
- Upload and Label Images:
- Upload a dataset of labeled images. You can use public datasets or your own set of images (e.g., pictures of different animals or objects).
-
Ensure a balanced number of images per class.
-
Train the Model:
- Train the image classification model using the labeled images.
-
Adjust training iterations for optimal performance.
-
Evaluate Model Performance:
- Use the platform’s tools to evaluate model accuracy.
-
Test the model with new images to see how well it generalizes.
-
Deploy and Test the Model:
- Deploy the model as an endpoint.
- Test the endpoint using REST API or the Custom Vision portal by sending a new image and receiving the classification result.
Tools: Azure Custom Vision, Azure Portal, REST API tools.
Outcome: Understand the process of creating, training, evaluating, and deploying a computer vision model using Azure's Custom Vision service.
4. Describe Features of Natural Language Processing (NLP) Workloads on Azure
Project: Sentiment Analysis with Azure Text Analytics
Objective: To use Azure Text Analytics to analyze the sentiment of customer reviews.
Steps: 1. Set Up Text Analytics Resource: - Create a Text Analytics resource in the Azure portal.
- Prepare Dataset:
- Use a sample dataset of customer reviews (e.g., product reviews from Amazon or Yelp).
-
Ensure that the dataset is in a text format (CSV or JSON).
-
Analyze Sentiment:
- Use the Azure Text Analytics API to analyze the sentiment of each review in the dataset.
-
Write a script in Python or use a Jupyter Notebook to send requests to the API and parse the responses.
-
Visualize Results:
- Aggregate the results to see the overall sentiment distribution (positive, neutral, negative).
-
Use a visualization tool (e.g., Matplotlib or Power BI) to create a bar chart or pie chart of the sentiment analysis results.
-
Analyze Key Phrases and Entities:
- Use the Text Analytics API to extract key phrases and named entities from the reviews.
- Identify common themes or trends in customer feedback.
Tools: Azure Text Analytics, Python, Jupyter Notebook, data visualization tools (Matplotlib, Power BI).
Outcome: Learn how to perform sentiment analysis and extract key insights from text data using Azure's NLP capabilities.
5. Describe Features of Conversational AI Workloads on Azure
Project: Build a Simple FAQ Bot using Azure Bot Service and QnA Maker
Objective: To create a basic chatbot that answers frequently asked questions (FAQ) using Azure Bot Service and QnA Maker.
Steps: 1. Set Up QnA Maker Resource: - Create a QnA Maker resource in the Azure portal.
- Create a Knowledge Base:
- Use a pre-existing FAQ document or create one with questions and answers relevant to a specific topic (e.g., company policies, product information).
-
Import this document into QnA Maker to create a knowledge base.
-
Train the QnA Maker:
- Train the QnA Maker with the uploaded questions and answers.
-
Test the knowledge base by asking sample questions to ensure it retrieves correct responses.
-
Deploy the Knowledge Base:
-
Publish the knowledge base to make it available for integration with the bot.
-
Create a Bot using Azure Bot Service:
- Use the Azure Bot Service to create a new bot and link it to your QnA Maker knowledge base.
-
Test the bot using the Web Chat feature in the Azure portal.
-
Enhance the Bot’s Capabilities:
- Add additional intents or use Language Understanding (LUIS) to enhance the bot’s ability to understand different queries.
- Integrate the bot with other channels like Microsoft Teams, Slack, or a web application.
Tools: Azure Bot Service, QnA Maker, Azure Portal.
Outcome: Learn how to create and deploy a simple FAQ bot using Azure's Conversational AI services, and understand the basics of bot development and integration.