Cloud-Native Python, DevOps & LLMOps. Containerization, Kubernetes, and Serving AI Models at Scale
Description
Your Code Works Locally. Now, Make It Run for the World.
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).
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.
Don't just write software. Architect it.
