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Duration 14 hours
Course Outline
Fundamentals of Azure Machine Learning
- Overview of AML features and architectural components
- Introduction to end-to-end workflows using AML (Azure ML pipelines)
- Guided navigation of Azure Machine Learning Studio
Data Preparation and Model Development
- Strategies for effective data preparation
- Steps involved in building a model
- Processes for training and testing models
Model Evaluation and Ensuring Robustness
- Applying validation metrics to assess ML models
- Techniques for handling and preventing overfitting
Model Management and Deployment Strategies
- Procedures for registering trained models
- Creating optimized model images
- Best practices for model deployment
Basics of the OpenAI API on Azure
- Introduction to the capabilities of the OpenAI API
- Setting up API configuration and handling authentication
Retrieval and Application Integration
- Leveraging documents with AI Search
- Methods for integrating OpenAI models into application architectures
Customization and Production-Ready Practices
- Implementing model fine-tuning and customization
- Adhering to best practices in production environments
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of Python and foundational machine learning concepts
- Practical experience working with REST APIs or SDKs
- Basic familiarity with the Azure service ecosystem
Target Audience
- Data scientists and ML engineers
- Application developers focused on building AI-driven features
- Technical leads and solution architects
