Description

Course Objectives

By the end of the course, participants will:

• Understand the principles of AI model
• Learn to integrate AI models into web applications and
• Optimize model performance for scalability and
• Implement monitoring and maintenance strategies for deployed
• Gain proficiency in using cloud platforms and tools for

Target Audience

• Data scientists and ML engineers transitioning to production
• Software developers with an interest in AI and
• Professionals looking to enhance their AI deployment

Prerequisites

• Basic knowledge of Python and machine
• Familiarity with frameworks like TensorFlow or PyTorch is
• Understanding of REST APIs and Docker is a

Syllabus

Day 1: Introduction to AI Model Deployment

• Overview of the model deployment
• Key challenges in deploying AI
• Tools and platforms for

Day 2: Preparing Models for Deployment

• Exporting and saving models (ONNX, SavedModel, ).
• Model versioning and compatibility
• Introduction to containerization with

Day 3: Creating REST APIs for AI Models

• Building APIs using Flask and
• Integrating AI models with
• Testing and debugging API

Day 4: Deploying Models on Local and Cloud Platforms

• Local deployment using Docker and
• Cloud deployment on AWS, Azure, and
• Using serverless services for model

Day 5: Real-Time vs. Batch Inference

• Understanding inference
• Strategies for handling real-time
• Batch processing pipelines with Apache

Day 6: Optimizing Model Performance

• Quantization, pruning, and
• Tools like TensorRT and ONNX
• Balancing accuracy and

Day 7: Monitoring and Logging

• Importance of monitoring in
• Setting up logging with tools like ELK Stack and
• Automated alerts and anomaly

Day 8: Scaling AI Systems

• Horizontal vertical scaling.
• Load balancing and auto-scaling in cloud
• Optimizing cost and resource

Day 9: Security and Ethical Considerations

• Securing APIs and
• Handling sensitive data and
• Avoiding biases in deployed

Day 10: Capstone Project and Case Studies

• End-to-end deployment of a real-world AI
• Presentation of projects and peer
• Discussion of industry case studies and best