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Duration 21 hours
Course Outline
Introduction to Google AI Studio
- Examining the scope and capabilities of Google AI Studio
- Configuring a workspace and familiarizing oneself with the interface
- Grasping AI project workflows within Google AI Studio
Data Preparation and Administration
- Importing and preprocessing datasets
- Investigating data visualization tools
- Maintaining data integrity for AI projects
Model Training and Refinement
- Utilizing AutoML for rapid model development
- Performing custom model training using TensorFlow and PyTorch
- Tuning hyperparameters and optimizing performance
Model Deployment and Scalability
- Exposing models as REST APIs
- Integrating models with Google Cloud infrastructure
- Scaling AI services for production environments
Utilizing Advanced Features
- Applying Explainable AI (XAI) methodologies
- Leveraging Google AI APIs for vision, language, and other domains
- Exploring pre-trained models and transfer learning techniques
Monitoring and Debugging
- Tracking deployed models to ensure optimal performance
- Interpreting model predictions and feedback loops
- Resolving common challenges in AI workflows
Practical Applications
- Analyzing case studies of AI solutions built with Google AI Studio
- Constructing a full AI project from inception to completion
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning principles and frameworks
- Proficiency in Python programming
- Prior experience with Google Cloud services is advised
Target Audience
- AI developers
- Machine learning engineers
- Data scientists
