- Preface
- 1. Python Essentials for DevOps
- 2. Automating Files and the Filesystem
- 3. Working with the Command Line
- 4. Useful Linux Utilities
- 5. Package Management
- 6. Continuous Integration and Continuous Deployment
- 7. Monitoring and Logging
- 8. Pytest for DevOps
- 9. Cloud Computing
- Cloud Computing Foundations
- Types of Cloud Computing
- Types of Cloud Services
- Infrastructure as Code
- Continuous Delivery
- Virtualization and Containers
- Challenges and Opportunities in Distributed Computing
- Python Concurrency, Performance, and Process Management in the Cloud Era
- Process Management
- Conclusion
- Exercises
- Case Study Questions
- 10. Infrastructure as Code
- A Classification of Infrastructure Automation Tools
- Manual Provisioning
- Automated Infrastructure Provisioning with Terraform
- Automated Infrastructure Provisioning with Pulumi
- Creating a New Pulumi Python Project for AWS
- Creating Configuration Values for the Staging Stack
- Provisioning an ACM SSL Certificate
- Provisioning a Route 53 Zone and DNS Records
- Provisioning a CloudFront Distribution
- Provisioning a Route 53 DNS Record for the Site URL
- Creating and Deploying a New Stack
- Exercises
- 11. Container Technologies: Docker and Docker Compose
- What Is a Docker Container?
- Creating, Building, Running, and Removing Docker Images and Containers
- Publishing Docker Images to a Docker Registry
- Running a Docker Container with the Same Image on a Different Host
- Running Multiple Docker Containers with Docker Compose
- Porting the docker-compose Services to a New Host and Operating System
- Exercises
- 12. Container Orchestration: Kubernetes
- Short Overview of Kubernetes Concepts
- Using Kompose to Create Kubernetes Manifests from docker-compose.yaml
- Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- Launching a GKE Kubernetes Cluster in GCP with Pulumi
- Deploying the Flask Example Application to GKE
- Installing Prometheus and Grafana Helm Charts
- Destroying the GKE Cluster
- Exercises
- 13. Serverless Technologies
- 14. MLOps and Machine learning Engineering
- What Is Machine Learning?
- Python Machine learning Ecosystem
- Cloud Machine learning Platforms
- Machine learning Maturity Model
- Machine Learning Key Terminology
- Level 1: Framing, Scope Identification, and Problem Definition
- Level 2: Continuous Delivery of Data
- Level 3: Continuous Delivery of Clean Data
- Level 4: Continuous Delivery of Exploratory Data Analysis
- Level 5: Continuous Delivery of Traditional ML and AutoML
- Level 6: ML Operational Feedback Loop
- Sklearn Flask with Kubernetes and Docker
- Sklearn Flask with Kubernetes and Docker
- Exercises
- Case Study Question
- Learning Assessments
- 15. Data Engineering
- 16. DevOps War Stories and Interviews
- Index
Python for DevOps
Learn Ruthlessly Effective Automation
Noah Gift, Kennedy Behrman, Alfredo Deza, and Grig Gheorghiu
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