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DSTC-00735 Online (e-LMS) Graduate / Intermediate

Containerization of AI Applications with Docker and Kubernetes Course

by - DSTC

Master Containerization of AI Applications with Docker and Kubernetes in 4 weeks through hands-on, project-based online training with DSTC.

★★★★★ Be the first to review • 4 Weeks · 40 hrs • e-Certificate Included
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From ₹5,500 + GST

Programme Parameters

Educational Level:
Graduate / Intermediate
Duration & Workload:
4 Weeks (40 Hrs)
Delivery Mode:
Online (e-LMS)
Prerequisites:
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.

About This 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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.

🎯 Program Aim

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.

📋 Course Objectives

1. Put AI in Industry & Manufacturing techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.

👥 Who Should Enroll?

• Master's and senior undergraduate students specializing in AI in Industry & Manufacturing
• R&D engineers and working professionals applying AI in Industry & Manufacturing in industry
• Academics and educators building research or teaching capacity in AI in Industry & Manufacturing

🚀 Key Learning Outcomes

• A portfolio-grade AI in Industry & Manufacturing deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.

💎 What You'll Gain

🎥

Live & Recorded Sessions

Lifetime access to class recordings
🎓

e-Certificate on Completion

Cryptographically verified credential
💬

Post-Programme Support

Direct access to mentors & council
💻

Hands-On Experience

Notebooks, real-world code & datasets

Curriculum Outline

Module 1 Outline

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 Outline

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 Outline

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 Outline

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 Outline

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 Outline

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 Outline

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 Outline

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

Technical Specifications

ParameterRequirement
Covered Tool / PlatformContainerization
Covered Tool / PlatformContinuous Integration
Covered Tool / PlatformDocker
Covered Tool / PlatformInfrastructure as Code
Covered Tool / PlatformKubernetes
Covered Tool / PlatformAI Deployment
Covered Tool / PlatformMLOps
Covered Tool / PlatformModel Serving
Covered Tool / PlatformCI/CD Pipelines
Covered Tool / PlatformCloud-Native AI

Frequently Asked Questions

The Containerization of AI Applications with Docker and Kubernetes course DSTC (DSTC) teaches how AI and machine learning applications can be packaged, deployed, scaled, and managed using modern container-based infrastructure. It covers containerization, Docker, Kubernetes, continuous integration, infrastructure as code, AI service deployment, model serving, and production-ready deployment workflows.

Yes. This course can be suitable for motivated beginners with basic knowledge of programming, AI or machine learning concepts, and software development workflows. DSTC starts with containerization fundamentals and gradually introduces Docker, Kubernetes, continuous integration, and infrastructure automation for AI application deployment.

AI models developed in notebooks or local environments often face challenges when moved into production. Containerization with Docker and orchestration with Kubernetes help solve portability, scalability, dependency management, reproducibility, versioning, and deployment reliability issues, making AI applications easier to operate across development, testing, and production systems.

This course can support career growth in MLOps, AI deployment, cloud DevOps, AI platform engineering, software engineering, data science operations, and cloud-native AI infrastructure. Learners with skills in Docker, Kubernetes, continuous integration, infrastructure as code, and scalable AI deployment can strengthen profiles for roles involving machine learning operations and production AI systems.

The course covers Containerization, Continuous Integration, Docker, Infrastructure as Code, and Kubernetes. Learners also explore Docker images, containers, volumes, networks, AI API deployment, model serving concepts, Kubernetes pods, services, deployments, scaling, load balancing, automated configuration, CI/CD pipelines, and cloud-native AI deployment workflows.

DSTC’s course stands out because it focuses specifically on containerizing and orchestrating AI applications rather than teaching only generic Docker or Kubernetes concepts. The program connects containerization, Docker packaging, Kubernetes orchestration, continuous integration, infrastructure as code, model serving, and AI deployment challenges in one structured learning pathway.

The course is structured as a 4-week online, instructor-led program. With consistent study and participation, learners can complete the modules, case studies, and applied deployment concepts within the program timeline.

The course includes technical deployment concepts, but it is structured step by step. Learners begin with containerization basics and progress toward Docker packaging, Kubernetes orchestration, continuous integration, and infrastructure automation. Students with basic programming or AI/ML exposure usually find the course manageable with consistent effort.

Yes. Upon successful completion, learners receive an official DSTC e-Certification + e-Marksheet. This credential validates learning in AI application containerization, Docker, Kubernetes, continuous integration, infrastructure as code, scalable deployment workflows, and production-ready AI systems.

Yes. The course is designed to help learners understand how AI models and services can move from development environments to scalable deployment workflows. Learners explore Docker-based packaging, Kubernetes-based orchestration, automated deployment practices, infrastructure consistency, monitoring considerations, and production-readiness concepts for AI applications.

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