Operational AI with Docker
Deploy, scale, and operate agentic AI services with Docker and Kubernetes
Created by Ajeet Singh Raina, Harsh Manvar
Learn how to deploy, scale, and operate AI services using Docker and Kubernetes. Move beyond experiments by building AI systems that are reliable, reproducible, and production-ready. Gain hands-on experience with containerized workflows, scalable infrastructure, and secure integration patterns.
Packt | Apr 2026 | 390 min
What You Will Learn
You will start by containerizing AI models and workflows, then move on to serving and managing them in production environments. Step by step, you will apply proven deployment patterns, integrate secure access to tools and data, and implement observability. By the end, you will confidently scale AI workloads using Docker and Kubernetes best practices.
Key Features
- Containerize and deploy GenAI services for consistent, repeatable production workflows
- Serve and manage large language models using Docker and OpenAI-compatible APIs
- Scale and monitor multi-agent AI workloads with Kubernetes deployment patterns
Target Audience
Designed for cloud engineers, DevOps professionals, SREs, and platform engineers who want to run GenAI workloads in real-world environments. If you are comfortable with the command line and basic service operations, you will be able to apply these skills. Prior experience with Docker or Kubernetes is helpful but not required.
