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Self Paced

Containerization of AI Applications with Docker and Kubernetes

Deploy and Scale AI Applications with the Power of Docker and Kubernetes

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
4 Weeks

About

The program covers the complete process of containerizing AI models and applications using Docker and orchestrating them with Kubernetes. Participants will learn the fundamentals of containerization, deploying AI models, managing dependencies, and scaling AI applications using Kubernetes in both on-premise and cloud environments.

Aim

To teach participants how to containerize AI applications using Docker and Kubernetes for scalable deployment and management. This program provides practical knowledge for ensuring that AI models and applications can run consistently across multiple environments with scalability, security, and ease of orchestration.

Program Objectives

Learn how to containerize AI models and applications using Docker.
Understand Kubernetes orchestration for scaling and managing AI applications.
Implement real-world deployment pipelines for AI solutions.
Gain proficiency in building Dockerfiles, managing dependencies, and scaling applications.
Learn best practices for handling large-scale AI applications in Kubernetes.

Program Structure

Module 1: Introduction to Containerization

Overview of Containerization and Virtualization
Benefits of Containers in AI/ML Workflows
Key Concepts: Containers, Images, and Registries
Introduction to Docker and Kubernetes

Module 2: Docker for AI Applications

Installing Docker and Docker Basics
Creating and Managing Docker Containers
Building Docker Images for Machine Learning Models
Dockerfile for AI Applications (Python, TensorFlow, PyTorch)
Case Study: Containerizing a Simple AI Model

Module 3: Docker Compose for Multi-Container AI Applications

Introduction to Docker Compose
Defining and Managing Multi-Container Applications
Linking AI Services (e.g., Model API, Database)
Building and Orchestrating AI Applications with Docker Compose

Module 4: Introduction to Kubernetes for AI Applications

Basics of Kubernetes Architecture (Pods, Nodes, Services)
Setting Up a Kubernetes Cluster
Deploying AI Applications in Kubernetes Pods
Kubernetes vs. Docker: When to Use What?

Module 5: Scaling AI Applications with Kubernetes

Horizontal and Vertical Scaling of AI Applications
Managing Large-Scale AI Workloads with Kubernetes
Auto-scaling AI Models Based on Load
Monitoring and Managing Kubernetes Clusters

Module 6: Orchestrating AI Applications with Kubernetes

Introduction to Kubernetes Deployments and Stateful Sets
Load Balancing and Service Discovery for AI APIs
Rolling Updates and Rollbacks in AI Models
Case Study: Deploying an AI Model in Kubernetes

Module 7: CI/CD for AI with Docker and Kubernetes

Integrating Docker and Kubernetes into CI/CD Pipelines
Automating Model Packaging, Testing, and Deployment
Tools for CI/CD in Kubernetes (Jenkins, GitLab CI, Argo)
End-to-End AI Model Deployment Workflow

Module 8: Security and Monitoring in AI Containerization

Security Best Practices for Docker and Kubernetes in AI Applications
Securing AI Models and Data Pipelines in Containers
Monitoring AI Applications with Kubernetes Dashboard and Prometheus
Logging and Debugging AI Applications in Kubernetes

Module 9: Final Project

Containerize and Deploy an AI Application Using Docker and Kubernetes
Scale the Application for High Availability and Performance
Document the Workflow from Containerization to Deployment
Present and Demonstrate the Solution

Participant’s Eligibility

AI engineers, data scientists, cloud architects, DevOps professionals looking to deploy scalable AI applications using Docker and Kubernetes.

Program Outcomes

Ability to containerize AI models and deploy them consistently across environments.
Proficiency in orchestrating and scaling AI applications using Kubernetes.
Skills in setting up Docker environments, managing containers, and deploying AI services.
Understanding of persistent storage, load balancing, and scaling in AI infrastructure.

Fee Structure

Standard Fee: INR ~~5,998~~ USD 90

Discounted Fee: INR 2,999 USD 45

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!

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Certificate

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.