AI Model Deployment and Serving

Aim

Program Objectives

Program Structure

Module 1: Introduction to AI Model Deployment

• Overview of Model Deployment and Serving
• Challenges in Deploying Machine Learning Models
• Differences Between Model Development and Deployment
• Key Concepts: Latency, Scalability, and Monitoring

Module 2: Model Serving Architectures

• Overview of Model Serving Architectures
• Batch vs. Real-Time Serving
• REST APIs for Model Deployment
• Microservices Architecture for AI Models

Module 3: Deploying Models on Cloud Platforms

• Cloud-Based Model Deployment (AWS, Google Cloud, Azure)
• Introduction to MLaaS (Machine Learning as a Service)
• Deploying Models with Docker and Kubernetes
• Case Study: Deploying a Model on AWS SageMaker

Module 4: Continuous Integration and Continuous Deployment (CI/CD) for ML

• Understanding CI/CD Pipelines for Machine Learning
• Automating Model Deployment Workflows
• Integrating CI/CD with Model Retraining
• Tools: Jenkins, GitHub Actions, and CircleCI

Module 5: Model Monitoring and Maintenance

• Monitoring Model Performance in Production
• Drift Detection: Data Drift and Concept Drift
• Automated Model Retraining and Updates
• Logging, Metrics, and Alerts for Model Health

Module 6: Model Optimization for Serving

• Model Compression Techniques (Quantization, Pruning)
• Optimizing Models for Edge Devices
• Reducing Latency with Batch Inference
• Tools for Model Optimization (TensorRT, ONNX)

Module 7: Security and Privacy in Model Deployment

• Securing Deployed Models: Authentication, Encryption
• Handling Sensitive Data in Model Serving
• GDPR and Data Privacy Concerns in AI
• Case Studies in Secure Model Deployment

Module 8: Final Project

• Design and Deploy a Machine Learning Model for Real-Time Serving
• Focus Areas: Cloud Deployment, CI/CD Pipeline, or Monitoring
• Present the Deployment Strategy, Challenges, and Solutions
• Evaluation Based on Practical Implementation and Scalability

Who Should Enrol?

Program Outcomes

Fee Structure

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