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Duration 14 hours
Course Outline
Azure Machine Learning Foundations
- Summary of Azure ML capabilities and underlying system architecture
- Explanation of the comprehensive workflow within Azure ML (Azure ML pipelines)
- Navigation and utilization of the Azure Machine Learning Studio interface
Data Processing and Model Construction
- Preparation and structuring of datasets
- Development of predictive or generative models
- Execution of training and validation phases
Model Assessment and Reliability
- Application of standard performance metrics for machine learning models
- Strategies for mitigating and preventing overfitting scenarios
Model Lifecycle Management and Release
- Registration of completed models into the registry
- Construction of containerized model images
- Deployment of models to production environments
Introduction to Azure OpenAI Services
- Fundamentals of the OpenAI Application Programming Interface (API)
- Configuration and secure authentication procedures
Data Retrieval and Application Connectivity
- Managing documents and indices via Azure AI Search
- Integration of Azure OpenAI models into existing application stacks
Model Adaptation and Operational Standards
- Processes for fine-tuning and customizing model behavior
- Establishment of best practices for production environments
Conclusions and Future Actions
Requirements
- Working knowledge of Python programming and fundamental machine learning principles
- Practical experience with REST APIs or Software Development Kits (SDKs)
- General familiarity with Azure service offerings
Intended Audience
- Data scientists and machine learning engineers
- Application developers implementing AI-driven features
- Technical leads and enterprise solution architects
Testimonials (1)
the instructor :)
