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Course Outline
Fundamentals of Azure Machine Learning
- Overview of AML features and architectural design.
- Understanding the end-to-end workflow in AML (Azure ML pipelines).
- Navigating the Azure Machine Learning Studio interface.
Data Preparation and Modeling
- Strategies for data preparation.
- Constructing a model.
- Training and testing methodologies.
Model Evaluation and Robustness
- Key validation metrics for ML models.
- Techniques for handling and preventing overfitting.
Model Management and Deployment
- Registering trained models.
- Creating model images.
- Deploying models to production.
OpenAI API Basics on Azure
- Introduction to the OpenAI API.
- API configuration and authentication processes.
Retrieval and Application Integration
- Utilizing AI Search for document management.
- Integrating OpenAI models into applications.
Customization and Production Practices
- Model fine-tuning and customization.
- Adhering to best practices in production environments.
Summary and Next Steps
Requirements
- Familiarity with Python and fundamental machine learning concepts.
- Experience working with REST APIs or SDKs.
- Basic knowledge of Azure services.
Target Audience
- Data scientists and ML engineers.
- Application developers creating AI-driven features.
- Technical leads and solution architects.
14 Hours
