16.3 Model Development and Deployment Patterns

In this section

• Model Training Pipeline Pattern: Automating and Scaling AI Model Training
  Explore the Model Training Pipeline Pattern in AI, focusing on automation, scalability, and best practices for efficient model training and deployment.
• AI Model Deployment and Serving Patterns: Strategies, Tools, and Best Practices
  Explore comprehensive strategies and best practices for deploying AI models, including real-time serving, edge deployment, and using tools like TensorFlow Serving. Learn about containerization, scaling, versioning, and MLOps for efficient AI model deployment.
• AI Model Monitoring and Maintenance Patterns: Ensuring Optimal Performance Post-Deployment
  Explore comprehensive strategies for monitoring and maintaining AI models post-deployment. Learn about key metrics, detecting model drift, setting up alerts, and automating retraining processes to ensure continued model performance.
• Model Explainability and Interpretation Pattern in AI Design
  Explore the importance and techniques of model explainability and interpretation in AI, with practical examples and ethical considerations.