Outline

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

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Introduction to Docker • Creating Docker Images for AI Applications • Managing Containers, Images, Volumes, and Networks • Best Practices for Docker-Based AI Application Packaging

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Structuring AI Applications for Deployment • Serving Machine Learning Models Through APIs • Managing Configuration, Dependencies, and Runtime Settings • Preparing AI Services for Scalable Deployment Environments

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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

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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

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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

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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