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Duration 14 hours
Course Outline
Foundations of Azure Machine Learning
- Understanding AML features and architecture
- Exploring end-to-end workflows within AML (Azure ML pipelines)
- Getting acquainted with Azure Machine Learning Studio
Data Preparation and Model Development
- Preparing data for analysis
- Constructing a model
- Training and testing the model
Evaluating Model Performance and Robustness
- Applying validation metrics to ML models
- Addressing and mitigating overfitting
Managing and Deploying Models
- Registering a trained model
- Generating a model image
- Executing model deployment
Basics of the OpenAI API on Azure
- Introduction to the OpenAI API
- Configuring APIs and managing authentication
Retrieval and Application Integration
- Utilizing documents with AI Search
- Incorporating OpenAI models into applications
Customization and Production Readiness
- Fine-tuning and customizing models
- Adhering to best practices in production environments
Recap and Future Directions
Requirements
- A solid grasp of Python and fundamental machine learning principles
- Practical experience working with REST APIs or SDKs
- Basic knowledge of Azure services
Target Audience
- Data scientists and ML engineers
- Application developers implementing 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
