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Course Outline
Introduction to Google AI Studio
- An overview of Google AI Studio and its core capabilities
- Setting up your workspace and exploring the user interface
- Understanding AI project workflows within Google AI Studio
Data Preparation and Management
- Importing and preprocessing datasets
- Exploring available data visualization tools
- Ensuring high data quality for AI projects
Model Training and Optimization
- Utilizing AutoML for rapid model development
- Custom model training using TensorFlow and PyTorch
- Hyperparameter tuning and performance enhancement
Model Deployment and Scaling
- Deploying models as REST APIs
- Integrating models with Google Cloud infrastructure
- Scaling AI services for production environments
Leveraging Advanced Features
- Implementing Explainable AI (XAI) practices
- Utilizing Google AI APIs for vision, language, and other domains
- Exploring pre-trained models and transfer learning techniques
Monitoring and Troubleshooting
- Monitoring deployed models for performance metrics
- Analyzing model predictions and feedback loops
- Resolving common issues within AI workflows
Real-World Applications
- Case studies of AI solutions driven by Google AI Studio
- Building a comprehensive AI project from inception to completion
Summary and Next Steps
Requirements
- A solid grasp of machine learning concepts and frameworks
- Proficiency in Python programming
- Familiarity with Google Cloud services is highly recommended
Target Audience
- AI developers
- Machine learning engineers
- Data scientists
21 Hours
