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Duration 14 hours
Course Outline
Core Concepts of Azure Machine Learning
- Key features and architectural overview of AML
- Understanding end-to-end processes within AML (Azure ML pipelines)
- Working effectively with Azure Machine Learning Studio
Data Engineering and Model Development
- Techniques for data preparation
- Constructing machine learning models
- Procedures for training and validating models
Assessing Model Quality and Stability
- Applying relevant validation metrics to ML models
- Strategies for detecting and mitigating overfitting
Model Governance and Release
- Process for registering trained models
- Generating optimized model images
- Deploying models to production environments
Foundations of the OpenAI API on Azure
- Introduction to the capabilities of the OpenAI API
- Managing API settings and authentication protocols
Search Retrieval and System Integration
- Utilizing documents with AI Search
- Incorporating OpenAI models into existing application architectures
Advanced Customization and Operational Excellence
- Refining models through fine-tuning and customization
- Implementing best practices for production stability
Wrap-up and Future Recommendations
Requirements
- Familiarity with Python and core machine learning principles
- Practical experience working with REST APIs or SDKs
- Foundational knowledge of the Azure ecosystem
Target Audience
- Data scientists and ML engineers
- Software developers focusing on AI-driven features
- Technical leads and solution architects
