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Course Outline
Overview of Google AI Studio
- Key features and functionalities
- Grasping the components of a workflow
- Exploring the Google AI model landscape
Creating AI Workflows
- Organizing end-to-end processes
- Selecting elements for automation
- Handling inputs, outputs, and parameters
Integrating Models and Using APIs
- Linking AI Studio with Google AI APIs
- Incorporating custom and external models
- Developing reusable components
Testing and Validation
- Formulating test scenarios
- Confirming workflow reliability
- Troubleshooting model interactions
Enhancing Performance
- Boosting response speed and efficiency
- Managing resource allocation
- Sizing workflows for production environments
Security and Compliance
- Managing access control and user roles
- Adhering to data protection standards
- Ensuring secure API communications
Monitoring and Maintenance
- Tracking workflow performance
- Analyzing logs and metrics
- Managing the lifecycle of deployed workflows
Expanding AI Studio Workflows
- Connecting with external tools
- Automating processes using cloud functions
- Adding functionality via third-party services
Summary and Future Directions
Requirements
- Knowledge of AI model development processes
- Familiarity with cloud-based platforms or tools
- Understanding of prompt engineering principles
Who Should Attend
- Members of AI operations teams
- DevOps engineers
- System administrators
14 Hours
