GCP Vertex AI

1. Introduction to Machine Learning and Artificial Intelligence

• Machine learning overview
• Artificial intelligence concepts and applications

2. Introduction to Google Cloud Platform (GCP)

• Overview of GCP services and their role in machine learning
• Understanding GCP infrastructure and storage options

3. Introduction to Vertex AI

• Overview of Vertex AI and its key features
• Understanding Vertex AI components and their functionalities

4. Data Preparation and Preprocessing

• Data collection, cleaning, and transformation techniques
• Exploratory data analysis and visualization

5. Training Models with AutoML

• Utilizing Vertex AutoML for automated model training
• Configuring AutoML pipelines and selecting models

6. Custom Model Development

• Building custom machine learning models using Vertex AI
• Implementing algorithms and fine-tuning hyperparameters

7. Model Evaluation and Validation

• Evaluating model performance and accurency
• Validation techniques, including cross-validation and holdout validation

8, Model Deployment and Serving

• Deploying models to Vertex AI for online and batch prediction
• Managing model versions, scaling, and resource allocation

9. Monitoring and Managing Models

• Monitoring model performance and health
• Addressing concept drift and model decay

10. Advanced Topics in Vertex AI

• Advanced model optimization techniques
• Model explainability and interpretability
• Federated learning and privacy-preserving machine learning

11. MLOps and Pipelines

• Implementing MLOps workflows in Vertex AI
• Creating machine learning pipelines using Vertex AI Pipelines

12. Model Governance and Compliance

• Ensuring model fairness, ethics, and compliance
• Mitigating risks and addressing bias in AI systems

13. Performance Optimization and Scalability

• Optimizing model performance and latency
• Scaling models for large-scale deployments

14. Troubleshooting and Debugging

• Identifying and resolving common issues in model training and deployment
• Debugging techniques and best practices

15. Real-world Projects and Case Studies

• Applying Vertex AI to real-world machine learning problems
• Working on projects and case studies to gain hands-on experience

16. Certification Preparation and Exam Readiness

• Reviewing exam topics and preparing for Google Cloud Certified - AI Engineer certification
• Practicing sample questions