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Duration 14 hours
Course Outline
Azure Machine Learning Fundamentals
- An introduction to AML features and core architecture.
- An overview of end-to-end workflows within AML (Azure ML pipelines).
- Guidance on navigating Azure Machine Learning Studio.
Data Preparation and Modeling
- Techniques for effective data preparation.
- Processes for constructing a model.
- Steps involved in training and testing a model.
Model Evaluation and Robustness
- Selection and application of validation metrics for ML models.
- Strategies for handling and preventing overfitting.
Model Management and Deployment
- Procedures for registering a trained model.
- Methods for creating a model image.
- Best approaches for deploying a model.
OpenAI API Basics on Azure
- An introductory look at the OpenAI API.
- Details on API configuration and authentication processes.
Retrieval and Application Integration
- Utilizing documents with AI Search.
- Strategies for integrating OpenAI models into broader applications.
Customization and Production Practices
- Techniques for model fine-tuning and customization.
- Essential best practices for production environments.
Summary and Next Steps
Requirements
- A solid grasp of Python and foundational machine learning principles.
- Practical experience working with REST APIs or SDKs.
- A basic understanding of the Azure service ecosystem.
Target Audience
- Data scientists and ML engineers.
- Application developers focused on incorporating AI functionalities.
- Technical leads and solution architects.
