Table of Contents

Mentor Based

AI Model Deployment and Serving

Deploy, Scale, and Manage AI Models for Real-Time Predictions

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Mentor Based

LEVEL
Moderate

DURATION
3 Weeks

About

This program focuses on the end-to-end process of AI model deployment, exploring cloud-based platforms, containerization, and model serving frameworks like TensorFlow Serving, Flask, and Kubernetes. Participants will gain hands-on experience in deploying models and managing them post-deployment for real-time or batch predictions.

Aim

To provide advanced knowledge on deploying and serving machine learning models in production environments. This program covers best practices for ensuring scalability, real-time inference, and continuous model management.

Program Objectives

Learn the complete process of deploying AI models in production.
Implement scalable and secure model serving systems.
Understand the differences between batch and real-time inference.
Explore cloud and containerization solutions for AI models.
Manage post-deployment challenges like model drift and retraining.

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

Intended For

Data scientists, AI engineers, DevOps professionals, and cloud engineers focused on model deployment and serving.

Program Outcomes

Mastery of deploying AI models using Docker and Kubernetes.
Ability to implement real-time and batch inference systems.
Skills to monitor and maintain deployed models in production.
Experience in handling model drift and retraining in production.

Mentors

AI Mentor

Rajnish Tandon

Bodhi Nexus (Founder)

Biography

AI Mentor

Pratish Jain

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Biography

AI Mentor

J. T. Sibychen

Cyber and Cloud Security Trainer

NIIT Foundation

Biography

More Mentors

Fee Structure

Fee: INR 8,499 USD 112

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

List of Currencies