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Duration 14 hours
Course Outline
Introduction to Google AI Studio
- Key features and functionalities
- Understanding the building blocks of workflows
- Discovering the Google AI model ecosystem
Designing AI Workflows
- Structuring comprehensive end-to-end workflows
- Selecting components for automation
- Controlling inputs, outputs, and parameters
Model Integration and API Usage
- Linking AI Studio with Google AI APIs
- Incorporating custom and third-party models
- Developing reusable components
Testing and Validation
- Developing test scenarios
- Verifying workflow stability
- Troubleshooting model interactions
Performance Optimization
- Enhancing response times and efficiency
- Managing resource utilization
- Scaling workflows for production environments
Security and Compliance
- Access control and user administration
- Data protection principles
- Safeguarding API communications
Monitoring and Maintenance
- Tracking workflow performance
- Logging and analytics
- Managing the lifecycle of deployed workflows
Extending AI Studio Workflows
- Integrating with external tools
- Automation via cloud functions
- Expanding capabilities with third-party services
Summary and Next Steps
Requirements
- A solid grasp of AI model development processes
- Practical experience with cloud-based tools or platforms
- Working knowledge of prompt engineering principles
Target Audience
- AI operations teams
- DevOps specialists
- System administrators
