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Course Outline
Introduction to Google AI Studio
- Functional overview of Google AI Studio and its operational capabilities for government
- Workspace configuration and navigation of the user interface
- Understanding project lifecycles within Google AI Studio
Data Preparation and Management
- Procedures for importing and preprocessing datasets
- Utilization of data visualization tools to support analysis
- Ensuring data integrity and quality for AI initiatives
Model Training and Optimization
- Accelerating development through AutoML
- Execution of custom model training using TensorFlow and PyTorch
- Hyperparameter tuning and performance optimization strategies
Model Deployment and Scaling
- Deployment of models as RESTful APIs
- Integration with Google Cloud infrastructure for government operations
- Scaling AI services to meet production requirements
Leveraging Advanced Features
- Implementation of Explainable AI (XAI) practices to support accountability
- Utilization of Google AI APIs for vision, language processing, and other domains
- Application of pre-trained models and transfer learning techniques
Monitoring and Troubleshooting
- Continuous monitoring of deployed models for performance metrics
- Analysis of model predictions and user feedback
- Resolution of common issues within AI workflows
Real-World Applications
- Case studies illustrating AI solutions enabled by Google AI Studio
- End-to-end execution of a complete AI project
Summary and Next Steps
Requirements
- Comprehensive knowledge of machine learning principles and associated frameworks
- Proficiency in Python programming languages
- Experience with Google Cloud Platform services is preferred
Intended Audience
- Artificial intelligence developers
- Machine learning engineers
- Data scientists
21 Hours