Mentor Based
Containerization of AI Applications with Docker and Kubernetes
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Course Overview
Program Syllabus
Module 1
About This Course
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.
Module 2
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.
Module 3
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.
Module 4
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
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Containerization of AI Applications with Docker and Kubernetes
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 5
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 6
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 7
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 8
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 9
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 10
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 11
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 12
Who Should Enrol?
AI engineers, data scientists, cloud architects, DevOps professionals looking to deploy scalable AI applications using Docker and Kubernetes.
Module 13
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.
Module 15
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 16
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 17
Publication Opportunity
Get published in a prestigious open-access journal.
Module 18
Centre of Excellence
Become part of an elite research community.
Module 19
Networking & Learning
Connect with global researchers and mentors.
Module 20
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 21
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