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Duration 14 hours
Course Outline
Getting Started with Google AI Studio
- Key features and functional capabilities
- Dissecting workflow architecture elements
- Navigating the Google AI model landscape
Architecting AI Workflows
- Structuring comprehensive end-to-end processes
- Selecting optimal components for automation
- Handling inputs, outputs, and variable parameters
Model Connectivity and API Integration
- Linking AI Studio with Google AI services
- Incorporating custom and third-party models
- Developing modular, reusable components
Quality Assurance and Validation
- Formulating robust test scenarios
- Verifying workflow consistency and reliability
- Troubleshooting model interactions
Enhancing Performance
- Optimizing response times and resource efficiency
- Strategic management of resource consumption
- Preparing workflows for production scale
Security Posture and Compliance
- Managing access controls and user permissions
- Adhering to data protection standards
- Safeguarding API interactions
Ongoing Monitoring and Upkeep
- Tracking workflow health and metrics
- Leveraging logs and analytical insights
- Managing the lifecycle of live workflows
Expanding Capabilities
- Connecting with external ecosystems
- Implementing automation via cloud functions
- Augmenting functionality through third-party integrations
Wrap-up and Future Directions
Requirements
- Fundamental grasp of AI model development lifecycles
- Proficiency with cloud-native tools or platforms
- Basic knowledge of prompt engineering techniques
Target Audience
- AI operations specialists
- DevOps engineers
- System administrators
