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Duration 14 hours
Course Outline
Azure Machine Learning Essentials
- Exploration of AML functionalities and architectural components
- Understanding end-to-end workflows within AML (Azure ML pipelines)
- Navigating the Azure Machine Learning Studio interface
Data Processing and Model Construction
- Techniques for data preparation
- Process of building a model
- Steps involved in training and testing a model
Assessing Model Quality and Stability
- Application of validation metrics for ML models
- Strategies to manage and avoid overfitting
Model Governance and Launch
- Process for registering a trained model
- Method for creating a model image
- Deployment procedures for models
OpenAI API Fundamentals on Azure
- Overview of the OpenAI API
- Setting up API configuration and authentication
Search Retrieval and System Integration
- Working with documents using AI Search
- Integrating OpenAI models into existing applications
Customization Strategies and Production Standards
- Techniques for model fine-tuning and customization
- Adherence to best practices in production environments
Recap and Future Directions
Requirements
- Proficiency in Python and foundational machine learning principles
- Practical experience with REST APIs or SDKs
- General knowledge of core Azure services
Target Audience
- Data scientists and machine learning engineers
- Application developers implementing AI-driven features
- Technical leads and solution architects
