| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | Docker Docker Compose Dockerfiles Kubernetes Helm Prometheus Grafana Jenkins GitLab CI Argo CD |
About the Containerization of AI Applications with Docker and Kubernetes Course
The program covers the complete process of containerizing AI models and applications using Docker and orchestrating them with Kubernetes.
Participants will master fundamentals of containerization, deploying AI models, managing dependencies, and scaling AI applications in both on‑premise and cloud environments.
Program Highlights
• Comprehensive coverage of Containerization of AI Applications with Docker and Kubernetes from fundamentals to advanced applications
• Hands-on projects and real-world case studies in ai
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Docker, Docker Compose, Dockerfiles, Kubernetes
• Career-oriented training for academic and professional growth in ai
Course Curriculum
Module 1: Introduction to Containerization
- Understand containerization vs virtualization
- Identify containers, images, and registries
- Explore benefits for AI/ML workflows
Module 2: Docker for AI Applications
- Install Docker and learn core commands
- Create and manage containers for AI workloads
- Build optimized Docker images for TensorFlow & PyTorch models
Module 3: Docker Compose for Multi‑Container AI
- Introduce Docker Compose syntax
- Define multi‑container AI stacks (API, DB, etc.)
- Link services and manage inter‑container networking
Module 4: Kubernetes Fundamentals for AI
- Explain pods, nodes, services architecture
- Set up a local Kubernetes cluster
- Deploy AI models as Kubernetes pods
Module 5: Scaling AI Applications with Kubernetes
- Implement horizontal & vertical scaling strategies
- Configure auto‑scaling based on request load
- Monitor cluster health for AI workloads
Module 6: Advanced Orchestration in Kubernetes
- Work with Deployments and StatefulSets
- Set up load balancing and service discovery for AI APIs
- Execute rolling updates and rollbacks for model versions
Module 7: CI/CD Pipelines for AI
- Integrate Docker & Kubernetes into CI/CD workflows
- Automate model packaging, testing, and deployment
- Utilize Jenkins, GitLab CI, and Argo for pipelines
Tools, Techniques, or Platforms Covered
Docker Docker Compose Dockerfiles Kubernetes Helm Prometheus Grafana Jenkins GitLab CI Argo CD
Real-World Applications
- Apply Containerization of AI Applications with Docker and Kubernetes skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical ai competencies
- Solve industry-relevant problems using Containerization of AI Applications with Docker and Kubernetes methodologies and tools
- Contribute to open-source projects and collaborative research in ai
- Prepare for competitive examinations, interviews, and professional certifications in ai
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
