Lesson - Model deployment

[carmengg]

Model deployment

Goal

To provide participants with a comprehensive understanding of the processes, tools, and best practices involved in deploying deep learning models for various applications.

Breakdown

1. Introduction to Model Deployment
   • What is model deployment and why is it important?
   • The lifecycle of a machine learning model: From development to deployment
2. Deployment Challenges
   • Model size and computational constraints
   • Real-time processing requirements
   • Scalability and handling large numbers of requests
3. Model Optimization for Deployment
   • Quantization: Reducing the precision of the model's weights
   • Pruning: Removing unnecessary weights or neurons
   • Knowledge distillation: Training a smaller model using a larger model's outputs
   • ONNX (Open Neural Network Exchange): A platform-neutral format for models
4. Deployment Platforms and Tools
   • Cloud Platforms: AWS SageMaker, Google AI Platform, Azure Machine Learning
   • Edge Deployment: TensorFlow Lite, PyTorch Mobile
   • Containers: Docker, Kubernetes for scalable deployments
   • Serving Frameworks: TensorFlow Serving, TorchServe
5. Monitoring and Maintaining Deployed Models
   • Importance of monitoring model performance in real-world scenarios
   • Tools for monitoring: Prometheus, Grafana, custom logging
   • Continuous learning: Updating the model with new data
   • Versioning: Managing different versions of deployed models
6. Security and Ethical Considerations
   • Protecting the model from adversarial attacks
   • Ensuring user data privacy and compliance with regulations (e.g., GDPR)
   • Ethical considerations: Bias in deployed models, transparency, and accountability
7. Case Study: Real-world Model Deployment
   • Walkthrough of a real-world scenario of deploying a deep learning model
   • Challenges faced, solutions implemented, and results achieved