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Course Outline
Introduction to Google AI Studio
- Overview of Google AI Studio and its capabilities
- Setting up a workspace and exploring the interface
- Understanding AI project workflows in Google AI Studio
Data Preparation and Management
- Importing and preprocessing datasets
- Exploring data visualisation tools
- Ensuring data quality for AI projects
Model Training and Optimisation
- Using AutoML for quick model development
- Custom model training with TensorFlow and PyTorch
- Hyperparameter tuning and performance optimisation
Model Deployment and Scaling
- Deploying models as REST APIs
- Integrating models with Google Cloud infrastructure
- Scaling AI services for production use
Leveraging Advanced Features
- Implementing Explainable AI (XAI) practices
- Utilising Google AI APIs for vision, language, and more
- Exploring pre-trained models and transfer learning
Monitoring and Troubleshooting
- Monitoring deployed models for performance
- Analysing model predictions and feedback
- Troubleshooting common issues in AI workflows
Real-World Applications
- Case studies of AI solutions powered by Google AI Studio
- Building a complete AI project from start to finish
Summary and Next Steps
Requirements
- Strong understanding of machine learning concepts and frameworks
- Experience with Python programming
- Familiarity with Google Cloud services is recommended
Audience
- AI developers
- Machine learning engineers
- Data scientists
21 Hours
