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Duration 14 hours
Course Outline
Azure Machine Learning Fundamentals
- Overview of AML features and architecture
- Overview of end-to-end workflows in AML (Azure ML pipelines)
- Navigating Azure Machine Learning Studio
Data Preparation and Modeling
- Data preparation
- Building a model
- Training and testing a model
Model Evaluation and Robustness
- Validation metrics for ML models
- Handling and preventing overfitting
Model Management and Deployment
- Registering a trained model
- Creating a model image
- Deploying a model
OpenAI API Basics on Azure
- Introduction to the OpenAI API
- API configuration and authentication
Retrieval and Application Integration
- Documents with AI Search
- Integrating OpenAI models into applications
Customization and Production Practices
- Model fine-tuning / customization
- Best practices in production
Summary and Next Steps
Requirements
- A solid grasp of Python and fundamental machine learning concepts
- Proficiency with REST APIs or SDKs
- Basic knowledge of Azure services
Target Audience
- Data scientists and ML engineers
- Application developers incorporating AI features
- Technical leads and solution architects
Testimonials (1)
The practices were quite good, and the instructor explains the topic very well.
Greydis Rondon - Chevron Global Technology Services Company
Course - Azure ML and Azure OpenAI: Building and Deploying AI Applications
Machine Translated
