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Duration 21 hours
Course Outline
Introduction to Google AI Studio
- Overview of Google AI Studio and its functional capabilities
- Configuring the workspace and navigating the user interface
- Understanding AI project workflows within Google AI Studio
Data Preparation and Management
- Importing and preprocessing datasets
- Utilizing data visualization tools for analysis
- Ensuring data integrity and quality for AI initiatives
Model Training and Optimization
- Leveraging AutoML for efficient model development
- Customizing model training using TensorFlow and PyTorch
- Tuning hyperparameters and optimizing performance
Model Deployment and Scaling
- Deploying models as RESTful APIs
- Integrating models with Google Cloud infrastructure
- Scaling AI services for production-grade operations
Leveraging Advanced Features
- Implementing Explainable AI (XAI) standards
- 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 operational performance
- Analyzing model predictions and user feedback
- Resolving common issues in AI workflows
Real-World Applications
- Case studies of AI solutions implemented via Google AI Studio
- Developing a complete AI project from conception to execution
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
