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Duration 14 hours
Course Outline
Core Azure Machine Learning Concepts
- Comprehensive overview of AML features and underlying architecture
- Introduction to end-to-end workflows within AML (Azure ML pipelines)
- Navigating the interface and tools of Azure Machine Learning Studio
Data Curation and Model Construction
- Strategies for effective data preparation
- Steps involved in building a model
- Processes for training and testing model performance
Assessing Model Quality and Stability
- Selecting and applying validation metrics for ML models
- Techniques for identifying, handling, and preventing overfitting
Managing and Launching Models
- Procedures for registering a trained model
- Generating model images for deployment
- Executing model deployment strategies
Foundations of the Azure OpenAI API
- Getting started with the OpenAI API
- Setting up API configuration and managing authentication
Search Retrieval and Application Integration
- Utilizing documents with Azure AI Search
- Seamlessly integrating OpenAI models into broader applications
Refinement and Production Standards
- Model fine-tuning and customization techniques
- Adhering to best practices for production environments
Conclusion and Future Directions
Requirements
- A solid grasp of Python and fundamental machine learning principles
- Practical experience interacting with REST APIs or SDKs
- Foundational knowledge of the Azure service ecosystem
Intended Audience
- Data scientists and ML engineers
- Application developers focused on incorporating AI functionalities
- Technical leads and solution architects
Testimonials (1)
the instructor :)
