| Module 1 | Understanding Common AI Workloads in Azure |
|---|---|
| Unit 1 | Intro to Azure AI-900 |
| Unit 2 | Understanding Common AI Workloads in Azure |
| Unit 3 | AI Workloads - Computer Vision |
| Unit 4 | AI Workloads - NLP |
| Unit 5 | AI Workloads - Document Processing |
| Unit 6 | AI Workloads - Generative AI |
| Unit 7 | AI Workloads - Summary |
| Unit 8 | AI Workloads - Demo Lab |
| Module 2 | Guiding Principles for Responsible AI |
| Unit 1 | Introduction To Guiding Principles for Responsible AI |
| Unit 2 | Responsible AI - Fairness in AI |
| Unit 3 | Responsible AI - Reliability and Safety |
| Unit 4 | Responsible AI - Privacy and Security |
| Unit 5 | Responsible AI - Inclusiveness |
| Unit 6 | Responsible AI - Transparency |
| Unit 7 | Responsible AI - Accountability |
| Unit 8 | Exam |
| Module 3 | Identifying Common Machine Learning Techniques |
| Unit 1 | Identifying Common Machine Learning Techniques |
| Unit 2 | Regression |
| Unit 3 | Classification |
| Unit 4 | Clustering |
| Unit 5 | Deep Learning Techniques |
| Unit 6 | Transformer Architecture |
| Module 4 | Machine Learning Model |
| Unit 1 | Overview of Machine Learning Model |
| Unit 2 | Data Preparation and Data Splitting |
| Unit 3 | Apply the Algorithm |
| Unit 4 | Inferencing |
| Unit 5 | Training and Validation of Datasets |
| Module 5 | Azure Machine Learning Capabilities |
| Unit 1 | Introduction to Azure Machine Learning Capabilities |
| Unit 2 | Azure AutoML |
| Unit 3 | Data and Compute Azure Services for Data Science and Machine Learning |
| Unit 4 | Model Management and Deployment Capabilities in Azure ML |
| Unit 5 | Exam |
| Module 6 | Labs |
| Unit 1 | Lab Demo - Setting up your Azure ML workspace |
| Unit 2 | Lab Demo - Exploring a Dataset - Identifying Features and Labels |
| Unit 3 | Lab Demo - Understanding Data Splits - Training and Validation Sets |
| Unit 4 | Lab Demo - Azure Automated ML Run |
| Module 7 | Computer Vision Workloads on Azure |
| Unit 1 | Introduction to Computer Vision Workloads on Azure |
| Unit 2 | What is Computer Vision |
| Unit 3 | Image Classification |
| Unit 4 | Object Detection |
| Unit 5 | OCR - Optical Character Recognition |
| Unit 6 | Facial Detection and Facial Analysis Solutions |
| Module 8 | Azure AI Vision Service and AI Face Detection Service |
| Unit 1 | Azure AI Vision Service and AI Face Detection Service |
| Unit 2 | Exam |
| Module 9 | Labs |
| Unit 1 | Provisioning the Azure AI Vision Resource & Azure AI Face Resource |
| Unit 2 | Image Classification |
| Unit 3 | Custom Image Classification |
| Unit 4 | Image Detection |
| Unit 5 | Optical Character Recognition |
| Unit 6 | Face Detection |
| Module 10 | Introduction to NLP |
| Unit 1 | Introduction to NLP |
| Unit 2 | Key Phase Extraction |
| Unit 3 | Named Entity Recognition - NER |
| Unit 4 | Sentiment Analysis |
| Unit 5 | Language Modeling |
| Unit 6 | Speech Recognition |
| Unit 7 | Translation |
| Module 11 | Introduction to NLP Workloads in Azure |
| Unit 1 | Introduction to NLP Workloads in Azure |
| Unit 2 | Azure AI Translator |
| Unit 3 | Azure AI Language |
| Unit 4 | Azure AI Speech Service |
| Unit 5 | Summary of Azure Tools for NLP |
| Unit 6 | Exam |
| Module 12 | Labs |
| Unit 1 | Azure AI Language Service Resource Group Setup |
| Unit 2 | Sentiment Analysis and Opinion Mining |
| Unit 3 | Key Phrases Extraction |
| Unit 4 | Language Detection |
| Unit 5 | Named Entity Recognition |
| Unit 6 | Text Summarization |
| Module 13 | Introduction to Generative AI |
| Unit 1 | Introduction to Generative AI |
| Unit 2 | Features of Generative AI Models |
| Unit 3 | Features of Generative AI Solutions |
| Unit 4 | Common Scenarios and Use Cases in Generative AI |
| Unit 5 | Responsible AI Considerations |
| Module 14 | Microsoft Azure Gen AI Services and Capabilities |
| Unit 1 | Intro to Microsoft Azure Gen AI Services and Capabilities |
| Unit 2 | Azure OpenAI Service |
| Unit 3 | Azure AI Foundry Platform |
| Unit 4 | Leveraging the AI Foundry Model Catalog |
| Unit 5 | Workflow, RAG, Fine Tuning for Specialization, and Scenarios |
| Unit 6 | Exam |
| Module 15 | Lab Demo - Azure AI Foundry - Portal Overview and Model Catalog |
| Unit 1 | Azure AI Foundry - Portal Overview and Model Catalog |
| Unit 2 | Azure AI Foundry - Playgrounds |
| Unit 3 | Build and Customize - Agents, Templates, Fine-tuning, Content Understanding |
| Unit 4 | Observe and Optimize - Tracing, Monitoring |
| Unit 5 | Protect and Govern |
| Unit 6 | Stored Completions, Batch Jobs, Vector Stores, Data Files |
| Unit 7 | My Assets - Models + Endpoints, Web Apps |
| Module 16 | Bonus Exams |
| Unit 1 | Exam 1 |
| Unit 2 | Exam 2 |
Your Guided Learning Pathway
In this 12 hour course your instructor will take you from beginner to intermediate level practitioner.
22.5K Students
