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Course Outline
Foundations of Azure Machine Learning
- Overview of AML capabilities and architecture
- Understanding end-to-end workflows within AML (Azure ML pipelines)
- Getting oriented with Azure Machine Learning Studio
Data Processing and Model Development
- Strategies for data preparation
- Constructing a model
- Training and testing model performance
Assessing Model Quality and Robustness
- Selecting appropriate validation metrics for ML models
- Mitigating and preventing overfitting
Managing and Deploying Models
- Registering a trained model
- Generating a model image
- Executing model deployment
Introducing the OpenAI API on Azure
- Getting started with the OpenAI API
- Setting up API configuration and authentication
Retrieval and Application Integration
- Utilizing AI Search for document retrieval
- Embedding OpenAI models into application architectures
Customization and Production Standards
- Techniques for model fine-tuning and customization
- Adopting best practices for production environments
Review and Future Directions
Requirements
- A solid grasp of Python and fundamental machine learning principles
- Practical experience working with REST APIs or SDKs
- Basic familiarity with the Azure service ecosystem
Target Audience
- Data scientists and ML engineers
- Application developers implementing AI features
- Technical leads and solution architects
14 Hours