Aim

The program aims to provide advanced knowledge on deploying and serving machine learning models in production environments. Participants will learn best practices to ensure scalability, real-time inference, and continuous model management, focusing on the practical aspects of AI model deployment.

Program Objectives

End-to-End Model Deployment: Learn the full process of deploying machine learning models in production.
Scalable and Secure Serving Systems: Implement scalable systems for secure model serving.
Real-Time vs. Batch Inference: Understand the differences and best practices for each.
Cloud and Containerization: Explore solutions using cloud platforms, Docker, and Kubernetes.
Post-Deployment Challenges: Manage model drift, retraining, and ongoing maintenance.

Program Structure

Module 1: Introduction to AI Model Deployment

Overview of Model Deployment and Serving
Key Challenges: From Development to Deployment
Concepts: Latency, Scalability, and Monitoring

Module 2: Model Serving Architectures

Batch vs. Real-Time Serving: Pros and Cons
REST APIs for Model Deployment
Microservices Architecture for Scaling AI Models

Module 3: Deploying Models on Cloud Platforms

Deploying Models in the Cloud (AWS, Google Cloud, Azure)
Introduction to Machine Learning as a Service (MLaaS)
Docker and Kubernetes for Model Deployment
Case Study: Deploying a Model on AWS SageMaker

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

Understanding CI/CD Pipelines for AI
Automating the Model Deployment Workflow
Integrating CI/CD with Model Retraining
Tools: Jenkins, GitHub Actions, CircleCI

Module 5: Model Monitoring and Maintenance

Monitoring Model Performance in Production
Drift Detection: Data Drift vs. Concept Drift
Automating Model Retraining and Updates
Logging, Metrics, and Alerts for Model Health

Module 6: Model Optimization for Serving

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

Module 7: Security and Privacy in Model Deployment

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

Module 8: Final Project

Design and Deploy a Machine Learning Model for Real-Time Serving
Focus: Cloud Deployment, CI/CD Pipeline, or Model Monitoring
Present Deployment Strategy, Challenges, and Solutions

Participant’s Eligibility

Data scientists, AI engineers, DevOps professionals, and cloud engineers interested in model deployment and serving.

Program Outcomes

Master Model Deployment: Master deploying AI models using Docker, Kubernetes, and cloud platforms.
Scalable Inference Systems: Implement real-time and batch inference for high-demand environments.
Post-Deployment Maintenance: Gain expertise in monitoring, managing, and retraining models in production.
Model Optimization: Learn techniques for compressing and optimizing models for faster performance.

Program Deliverables

e-LMS Access: All course materials available online.
Real-Time Project: Practical project on deploying AI models in production.
Guidance: Project and dissertation guidance from industry experts.
Certification: Upon successful completion of all assessments.

Future Career Prospects

AI DevOps Engineer
Cloud AI Engineer
Model Deployment Engineer
AI Infrastructure Specialist
AI Solutions Architect

Job Opportunities

Cloud Computing Firms: Companies offering AI deployment services.
AI Startups: Companies needing scalable model serving solutions.
Large Enterprises: Businesses deploying AI models for real-time applications.
Data Science Teams: Teams focused on model maintenance, drift detection, and monitoring.

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	3 Weeks

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AI Model Deployment and Serving Course

Aim

Program Objectives

Program Structure