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Cloud-Native Python, DevOps & LLMOps. Containerization, Kubernetes, and Serving AI Models at Scale

From Docker and Kubernetes to Serving LLMs with Pulumi

Your Code Works Locally. Now, Make It Run for the World. 🌍 Mastered Python? Now face the ultimate challenge: Production. Volume 8: Cloud-Native Python, DevOps & LLMOps takes you beyond the IDE and into the data center. Learn to containerize with Docker, orchestrate with Kubernetes, and serve 70B+ parameter models with enterprise-grade LLMOps. Don’t just write software—architect it.

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About

About the Book

Your Code Works Locally. Now, Make It Run for the World.

You have mastered Python syntax, built web apps, and trained neural networks in the previous volumes. Now, you face the ultimate challenge: Production.

Volume 8: Cloud-Native Python, DevOps & LLMOps moves beyond the IDE to the data center. It is a comprehensive guide to architecting, deploying, and scaling Python applications in the modern cloud. This book bridges the gap between Software Development and Operations, with a specialized focus on the exploding field of LLMOps (Large Language Model Operations). You can read it as a standalone.

What You Will Build:

  • The Container Engine: Master Docker to create immutable, lightweight Python environments. Use Multi-Stage Builds to strip bloat and secure your supply chain.
  • The Orchestrator: Conquer Kubernetes. Learn the physics of Pods, Deployments, and Services. Package complex apps with Helm Charts and implement Horizontal Pod Autoscaling (HPA).
  • Infrastructure as Software: Stop writing YAML. Use Pulumi and Boto3 to provision AWS VPCs, EKS clusters, and S3 buckets using pure Python code.
  • Serverless Architecture: Build event-driven microservices using AWS Lambda, SQS, and SNS. Decouple your systems to handle infinite scale.
  • LLMOps & AI Serving: Deploy the heavy artillery. Configure the NVIDIA Container Toolkit for GPU passthrough. Serve 70B+ parameter models using vLLM with PagedAttention and TorchServe for enterprise-grade inference.
  • Self-Healing Infrastructure: Implement AIOps pipelines that use Machine Learning to analyze logs, detect anomalies via AWS X-Ray, and automatically repair the system.

Who This Book Is For:

Written for Python developers, ML Engineers, and aspiring Cloud Architects who want to stop "just writing code" and start building resilient, global platforms. If you want to know how to take a raw Python script and turn it into a scalable, GPU-accelerated cloud service, this is your blueprint.

All the source code is on GitHub.

Don't just write software. Architect it.

Check also the other books in this series

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Author

About the Author

Edgar Milvus

A veteran software engineer with 20 years of experience, I have dedicated my career to the art of automation. My philosophy is simple: programming should eliminate repetitive chores to unlock human creativity. This journey began early on with the development of custom code-generation tools and has evolved into a deep mastery of LLMs and their APIs. Today, I specialize in architecting AI-driven solutions that handle everything from complex coding and security tasks to advanced knowledge retrieval, transforming the way we interact with technology

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