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Containerization of AI Applications with Docker and Kubernetes Course
Original price was: INR ₹5,998.00.INR ₹2,999.00Current price is: INR ₹2,999.00.
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
This program is designed to teach professionals how to containerize AI applications using Docker and Kubernetes for scalable deployment and management. Participants will gain practical knowledge in ensuring AI models and applications can run efficiently across multiple environments with enhanced scalability, security, and orchestration.
Program Objectives
- Master Docker for AI: Learn how to containerize AI models and applications using Docker.
- Kubernetes Orchestration: Understand Kubernetes for scaling and managing AI applications.
- Build Real-World AI Pipelines: Implement deployment pipelines for AI solutions.
- Hands-On Docker Skills: Develop proficiency in building Dockerfiles, managing dependencies, and scaling AI applications.
- Best Practices for AI at Scale: Learn how to handle large-scale AI applications in Kubernetes.
Program Structure
Module 1: Introduction to Containerization
- Understanding Containerization and Virtualization
- Key Concepts: Containers, Images, and Registries
- Benefits of Containers in AI/ML workflows
Module 2: Docker for AI Applications
- Installing Docker and Docker Basics
- Creating and Managing Docker Containers
- Building Docker Images for AI Models
- Writing Dockerfiles for Python-based AI applications like TensorFlow and 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 AI Applications
- Linking AI services (Model API, Database)
Module 4: Introduction to Kubernetes for AI Applications
- Kubernetes Architecture: Pods, Nodes, and Services
- Setting up a Kubernetes Cluster
- Deploying AI Models in Kubernetes Pods
- Kubernetes vs Docker: Key Differences and Use Cases
Module 5: Scaling AI Applications with Kubernetes
- Horizontal and Vertical Scaling for AI Models
- Auto-scaling AI Models Based on Load
- Monitoring Kubernetes Clusters for AI Workloads
Module 6: Orchestrating AI Applications in Kubernetes
- Kubernetes Deployments and Stateful Sets
- Load Balancing and Service Discovery for AI APIs
- Rolling Updates and Rollbacks for AI Models
- Case Study: Deploying an AI model on 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 like Jenkins, GitLab CI, and Argo for CI/CD
Module 8: Security and Monitoring in AI Containerization
- Security Best Practices for AI Applications in Docker and Kubernetes
- Securing AI Models and Data Pipelines in Containers
- Monitoring AI Applications with Kubernetes Dashboard, Prometheus
- Logging and Debugging AI Containers
Module 9: Final Project
- Containerize and Deploy an AI Application Using Docker and Kubernetes
- Scale the Application for High Availability
- Document the Workflow from Containerization to Deployment
- Present and Demonstrate the Solution
Participant’s Eligibility
- AI Engineers looking to streamline model deployment.
- Data Scientists who want to understand the practical side of deploying AI applications.
- Cloud Architects focused on scalable AI solutions.
- DevOps Professionals handling AI model deployment and management.
Program Outcomes
- Containerization Mastery: Ability to containerize AI models for consistent deployment across environments.
- Kubernetes Proficiency: Skills in orchestrating and scaling AI applications using Kubernetes.
- Hands-On Docker Experience: Expertise in managing Docker environments and deploying AI services.
- Scalable AI Infrastructure: Understanding persistent storage, load balancing, and scaling AI infrastructure in Kubernetes.
Program Deliverables
- e-LMS Access: All course content accessible online.
- Real-Time Project: Practical experience containerizing and deploying AI applications.
- Guidance and Support: Mentorship for project development.
- Certification: Certification awarded after successfully completing all assignments and exams.
Future Career Prospects
- AI Infrastructure Engineer
- MLOps Engineer
- DevOps Specialist for AI Applications
- Kubernetes Engineer
- Cloud AI Architect
- AI Solutions Architect
Job Opportunities
- AI-Driven Companies: Focused on containerized AI solutions.
- Cloud Service Providers: Supporting Docker and Kubernetes infrastructures.
- Enterprises: Deploying scalable AI services.
- DevOps Teams: Requiring orchestration for AI models.
| MODE | Online/ e-LMS |
|---|---|
| TYPE | Self Paced |
| LEVEL | Moderate |
| DURATION | 4 Weeks |
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