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Duration 14 hours
Course Outline
Introduction to Google AI Studio
- Key features and capabilities
- Understanding workflow components
- Overview of the Google AI model ecosystem
Constructing AI Workflows
- Structuring end-to-end workflows
- Selecting components for automation
- Managing inputs, outputs, and parameters
Model Integration and API Utilisation
- Linking AI Studio with Google AI APIs
- Incorporating custom and third-party models
- Creating reusable components
Testing and Validation
- Developing test scenarios
- Verifying workflow reliability
- Troubleshooting model interactions
Performance Enhancement
- Improving response speed and efficiency
- Optimising resource usage
- Scaling workflows for production environments
Security and Compliance
- Access control and user management
- Data protection principles
- Safeguarding secure API communication
Monitoring and Maintenance
- Tracking workflow performance
- Logging and analytics
- Managing the lifecycle of deployed workflows
Expanding AI Studio Capabilities
- Integration with external tools
- Automation via cloud functions
- Augmenting functionality with third-party services
Conclusion and Future Steps
Requirements
- A solid grasp of AI model development workflows
- Hands-on experience with cloud-based tools or platforms
- Working knowledge of prompt engineering concepts
Target Audience
- AI operations teams
- DevOps professionals
- System administrators
