AI Model Deployment and Serving

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

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

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.

What you will learn?

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.