| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $59 |
| Tools | Containerization Continuous Integration Docker Infrastructure as Code Kubernetes AI Deployment MLOps Model Serving CI/CD Pipelines Cloud-Native AI |
About the Containerization of AI Applications with Docker and Kubernetes Course
The Containerization of AI Applications with Docker and Kubernetes course is an intermediate-level program designed to provide learners with a structured understanding of how AI applications can be packaged, deployed, scaled, and managed using modern container-based infrastructure. The course focuses on building reliable deployment workflows for machine learning models, AI services, APIs, and production-ready applications.
This program introduces learners to the principles of containerization, application packaging, reproducible environments, deployment automation, orchestration, scaling, and infrastructure management. Learners will explore how Docker and Kubernetes help teams move AI applications from development environments to production systems with improved consistency, portability, and operational efficiency.
Program Highlights
• Mentorship by industry experts and NSTC faculty
• Structured learning in AI application containerization and deployment workflows
• Hands-on conceptual exposure to Docker-based packaging and Kubernetes-based orchestration
• Case studies on deploying AI models, APIs, and scalable application services
• Practical understanding of continuous integration for automated testing and deployment
• Focus on reproducibility, scalability, infrastructure automation, and production readiness
• e-Certification + e-Marksheet upon successful completion
Course Curriculum
Module 1: Introduction to AI Application Deployment
- Overview of AI Application Deployment Challenges
- Need for Reliable and Reproducible Environments
- Role of Containerization in Modern AI Workflows
- Applications in Machine Learning Services, APIs, and Production Systems
Module 2: Fundamentals of Containerization
- Introduction to Containerization
- Benefits of Isolated and Portable Application Environments
- Packaging AI Applications with Dependencies and Runtime Requirements
- Containerization for Development, Testing, and Production Workflows
Module 3: Docker for AI Applications
- Introduction to Docker
- Creating Docker Images for AI Applications
- Managing Containers, Images, Volumes, and Networks
- Best Practices for Docker-Based AI Application Packaging
Module 4: Building Production-Ready AI Services
- Structuring AI Applications for Deployment
- Serving Machine Learning Models Through APIs
- Managing Configuration, Dependencies, and Runtime Settings
- Preparing AI Services for Scalable Deployment Environments
Module 5: Continuous Integration for AI Workflows
- Introduction to Continuous Integration
- Automating Build, Test, and Deployment Pipelines
- Version Control, Testing, and Validation in AI Application Delivery
- Improving Reliability Through Continuous Integration Practices
Module 6: Kubernetes for AI Application Orchestration
- Introduction to Kubernetes
- Deploying Containerized AI Applications on Kubernetes
- Pods, Services, Deployments, Scaling, and Load Balancing Concepts
- Managing Availability and Reliability in Kubernetes-Based Systems
Module 7: Infrastructure as Code
- Introduction to Infrastructure as Code
- Managing Deployment Environments Through Automated Configuration
- Reproducible Infrastructure for AI Applications
- Benefits of Infrastructure as Code in Scalable AI Operations
Module 8: Case Studies, Challenges, and Future Opportunities
- Case Studies in Docker and Kubernetes-Based AI Deployment
- Challenges in Scaling, Monitoring, Security, and Resource Management
- Operational Considerations for AI Applications in Production
- Future Opportunities in Cloud-Native AI and Automated Infrastructure Workflows
Tools, Techniques, or Platforms Covered
Containerization Continuous Integration Docker Infrastructure as Code Kubernetes AI Deployment MLOps Model Serving CI/CD Pipelines Cloud-Native AI
Real-World Applications
- Packaging AI applications into portable and reproducible containers
- Deploying machine learning models and AI APIs using Docker-based workflows
- Scaling AI services using Kubernetes-based orchestration
- Improving deployment reliability through continuous integration pipelines
- Managing infrastructure consistently through infrastructure as code practices
- Supporting production-ready AI systems with automated deployment and monitoring workflows
- Reducing environment-related issues across development, testing, and production systems
Who Should Attend & Prerequisites
- Designed for students, developers, AI learners, data science professionals, DevOps learners, software engineers, cloud technology learners, and industry participants interested in deploying AI applications using container-based infrastructure.
- Suitable for learners from computer science, artificial intelligence, data science, software engineering, cloud computing, DevOps, information technology, and related fields.
Certification
