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Duration 14 hours
Course Outline
Core Principles of Azure Machine Learning
- Overview of AML capabilities and underlying architecture
- Understanding the end-to-end workflow within AML (Azure ML pipelines)
- Effective navigation of Azure Machine Learning Studio
Data Processing and Modeling
- Preparing and structuring data
- Constructing the model architecture
- Executing training and testing cycles
Model Assessment and Reliability
- Applying validation metrics to ML models
- Managing and mitigating overfitting
Model Lifecycle Management and Deployment
- Registering trained models in the registry
- Generating model images
- Deploying models to production environments
Foundations of the OpenAI API on Azure
- Introduction to the OpenAI API ecosystem
- Configuring and authenticating API connections
Retrieval-Augmented Generation and Application Integration
- Utilizing documents with Azure AI Search
- Integrating OpenAI models into application layers
Advanced Customization and Production Best Practices
- Fine-tuning and customizing model behavior
- Implementing best practices for production stability
Key Takeaways and Future Pathways
Requirements
- A solid grasp of Python and fundamental machine learning principles
- Practical experience with REST APIs or SDKs
- Basic knowledge of the Azure service ecosystem
Intended Audience
- Data scientists and ML engineers
- Application developers incorporating AI functionalities
- Technical leads and solution architects
