Module 1: Introduction
- What is AI and Machine Learning?
- Key concepts in AI: Supervised, Unsupervised, and Reinforcement Learning
- Core GCP services relevant to AI: Compute Engine, Storage, BigQuery, and more
- Why Choose GCP for AI?
- Benefits of using GCP for AI and ML projects
- Comparison with other cloud providers
Module 2: Understanding GCP AI Tools and Services
- What is Vertex AI?
- Overview of Vertex AI components: Pipelines, Workbench, and Training
- Pre-built AI Services
- Google Cloud Vision API
- Google Cloud Natural Language API
- Google Cloud Speech-to-Text and Text-to-Speech APIs
- Google Cloud Translation API
- Types of AI Workflows on GCP
- Data preparation and training
- Model deployment and monitoring
- Integration and scaling
Module 3: Data Management and Preparation
- Data Storage Solutions
- Google Cloud Storage: Overview and use cases
- BigQuery: Data warehousing and SQL-based analytics
- Data Preparation and ETL Processes
- Google Cloud Dataflow: Data processing and pipeline management
- Google Cloud Dataprep: Data cleaning and preparation
- Managing Large Datasets
- Data ingestion and integration strategies
- Best practices for handling large volumes of data
Module 4: Building and Training AI Models
- Vertex AI Workbench
- Setting up and using Jupyter notebooks
- Managing and using environments for development
- Training Custom Models
- Using TensorFlow and PyTorch on GCP
- Leveraging AutoML for custom model training
- Hyperparameter Tuning
- Techniques for tuning models
- Vertex AI's hyperparameter tuning capabilities
Module 5: Model Deployment and Management
- Deploying Models on GCP
- Deploying models using Vertex AI
- Real-time and batch predictions
- Model Monitoring and Maintenance
- Monitoring model performance and logging
- Managing model versions and updates
- Scaling AI Solutions
- Strategies for scaling AI applications
- Load balancing and resource optimization
Module 6: Security and Compliance
- Data Security Best Practices
- Encrypting data at rest and in transit
- Implementing access controls and IAM policies
- Compliance and Privacy
- Understanding GCP's compliance certifications
- Best practices for data protection and privacy
Module 7: Integrating AI into Applications
- Building AI-Driven Applications
- Integrating GCP AI APIs into web and mobile apps
- Case studies of AI application in different industries
- Big Data Analytics with AI
- Using BigQuery ML for predictive analytics
- Combining AI with big data tools for enhanced insights
Module 8: Advanced Topics and Future Trends
- Advanced AI Techniques
- Transfer learning, Generative Adversarial Networks (GANs), and Reinforcement Learning
- Future Trends in AI
- Emerging technologies and innovations
- The future of AI on GCP and beyond
Module 9: Hands-On Labs and Projects
- Practical Exercises
- Building and deploying a sample AI model
- Integrating AI APIs into a sample application
- Capstone Project
- End-to-end project involving data preparation, model training, deployment, and integration
- Real-world scenario and problem-solving
