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Course Outline
Fundamentals of Azure Machine Learning
- Survey of Azure Machine Learning capabilities and underlying architecture
- End-to-end workflow using Azure ML pipelines
- Navigating the Azure Machine Learning Studio interface
Data Preparation and Model Development
- Data preparation processes
- Constructing machine learning models
- Training and evaluating model performance
Model Validation and Robustness
- Evaluation metrics for machine learning models
- Techniques to identify and mitigate overfitting
Model Governance and Deployment
- Registering trained models within the registry
- Building model containers (images)
- Deploying models to production environments for government
Basics of the OpenAI API on Azure
- Introduction to the OpenAI API capabilities
- Configuring and authenticating API access
Retrieval-Augmented Generation and Application Integration
- Ingesting documents via Azure AI Search
- Integrating OpenAI models into enterprise applications
Model Customization and Production Standards
- Fine-tuning and customizing models for specific needs
- Best practices for production-grade deployments
Program Summary and Forward Actions
Requirements
- Proficiency in Python programming and foundational machine learning principles
- Working knowledge of REST APIs or software development kits (SDKs)
- Elementary familiarity with Microsoft Azure cloud services
Audience
- Data scientists and machine learning engineers
- Application developers integrating artificial intelligence capabilities
- Technical leads and solution architects
14 Hours
Testimonials (1)
the instructor :)
