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.
Table of contents
Chapter 1: It Works on My Machine? - The Need for Containers
Chapter 2: Dockerizing Python - Writing Efficient Dockerfiles and Multi-Stage Builds
Chapter 3: Managing Dependencies - Poetry, pip-tools, and Reproducible Builds
Chapter 4: Orchestrating Services - Docker Compose for Python, Redis, and Postgres
Chapter 5: GPU Containers - Setting up NVIDIA Container Toolkit for AI Workloads
Chapter 6: Introduction to the Cloud - IAM, S3, and EC2 Basics via Boto3
Chapter 7: Serverless Python - AWS Lambda and Azure Functions
Chapter 8: Event-Driven Architecture - SQS, SNS, and Triggers
Chapter 9: API Gateway - Exposing Serverless Functions to the World
Chapter 10: Monitoring the Cloud - CloudWatch, Logging, and X-Ray Tracing
Chapter 11: The Orchestrator - Understanding Pods, Deployments, and Services
Chapter 12: Declaring State - YAML Configurations vs. Python Clients
Chapter 13: Scalability - Horizontal Pod Autoscaling (HPA) with Python Apps
Chapter 14: Managing Secrets - Safely Injecting Credentials into Clusters
Chapter 15: Helm Charts - Packaging Python Applications for Kubernetes
Chapter 16: Serving Large Models - Introduction to vLLM and TorchServe
Chapter 17: Vector Database Infrastructure - Deploying Chroma/Weaviate on Kubernetes
Chapter 18: AIOps - Using AI to Analyze Logs and Detect Anomalies (Self-Healing Infrastructure)
Chapter 19: Infrastructure as Software - Using Pulumi to Provision Cloud Resources with Python
Chapter 20: The Capstone - Building a Scalable 'Private ChatGPT' Platform on AWS
If printed, this ebook would span over 400 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.
Check also the other books in this series
