DevOps
DevOps is a set of organizational, cultural, and technical practices that integrates software development and IT operations through collaboration, automation, and feedback.
Effective Platform Engineering
DevOps is a cultural and organizational approach that integrates software development and IT operations through shared responsibility for building, deploying, and operating software.
- How it is used
- Integrates development and operations to accelerate delivery, improve collaboration, and establish shared ownership of running software.
- How deep it goes
- Defines cultural principles, team structures, automation, and failure modes, but stops short of detailed implementation internals.
- Examples
- Uses a cross-functional customer-account team, lifecycle diagrams, delivery metrics, automation scripts, and CI/CD workflow prototypes.
- What you can do after
- Apply it via cross-functional ownership and CI/CD automation to release software faster and resolve operational problems with fewer handoffs.
Delivering Machine Learning Projects: From design to deployment
DevOps is a software delivery practice that integrates development and operations work through collaboration, automation, and shared responsibility.
- How it is used
- Coordinates development, operations, automation, and production delivery to support rapid adaptation in machine-learning projects.
- How deep it goes
- Explains the development–operations mismatch, automation rationale, and project artifacts, but does not specify implementation technologies.
- Examples
- Uses generic delivery processes, sprints, backlogs, checklists, documentation portfolios, and the Bike Shop case study.
- What you can do after
- Apply it by automating delivery work, organizing ML tasks into sprints, and preserving documentation for production governance.
Read how Delivering Machine Learning Projects: From design to deployment explains DevOps →
Automating API Delivery
DevOps is a software delivery approach that integrates development and operations through lean principles, automation, feedback, and continuous learning.
- How it is used
- Addresses friction between API development and operations teams when scaling delivery without sacrificing quality.
- How deep it goes
- Defines three governing principles and describes version control and CI/CD practices, without specifying commands or pipeline configuration.
- Examples
- Uses a growing organization's API workflow to illustrate friction between development and platform or operations teams.
- What you can do after
- Evaluate/understand how DevOps practices can reduce API delivery lead times while limiting change failures.
Think Like a Software Engineering Manager
DevOps is a set of practices that integrates software development and IT operations to deliver and operate software reliably through collaboration, automation, and continuous feedback.
- How it is used
- Integrates development and operations to deliver stable systems, reduce friction, improve recovery, and support continuous operational improvement.
- How deep it goes
- Explains goals, practices, trade-offs, and adoption questions, but stops short of prescribing one universal implementation.
- Examples
- Uses runbooks, on-call processes, logs, memory-dump analysis, Netflix automation, cloud migration, and Chaos Monkey resilience testing.
- What you can do after
- Apply DevOps by identifying pain points, planning improvements, automating delivery, testing resilience, and tracking operational outcomes.
Read how Think Like a Software Engineering Manager explains DevOps →
AI for Everyday IT
DevOps is a set of practices that integrates software development and IT operations through collaboration, automation, continuous delivery, and shared responsibility.
- How it is used
- Integrates AI into existing DevOps workflows for infrastructure, testing, code review, and continuous delivery.
- How deep it goes
- Demonstrates practical applications and career planning but stops short of explaining DevOps internals.
- Examples
- Uses infrastructure automation, PowerShell test conversion, code review, AI testing, and structured skill-gap prompts.
- What you can do after
- Apply AI tools to automate infrastructure, update tests, improve CI/CD work, and plan relevant skill development.
Introduction to Generative AI, Second Edition
DevOps is a software-engineering practice that integrates development and operations to automate and improve software delivery and system management.
- How it is used
- Deploys AI agents in DevOps and CI/CD workflows to monitor systems and assist operational response.
- How deep it goes
- Describes operational tasks and human oversight but stops short of implementation details or a complete DevOps process.
- Examples
- Uses monitoring, failing-test fixes, deployment rollbacks, and performance triage as concrete examples.
- What you can do after
- Apply it via AI agents to monitor systems, address failures, roll back deployments, and triage performance issues under human oversight.
Read how Introduction to Generative AI, Second Edition explains DevOps →
LLMs in Production
DevOps is the set of practices that integrates software development and IT operations to automate and improve software delivery and operation.
- How it is used
- Provides the environmental resources and operational systems required to develop, stage, deploy, and run software in production.
- How deep it goes
- Defines responsibilities and names infrastructure categories but stops short of detailed implementation procedures.
- Examples
- Uses GitHub or GitLab, artifact and container registries, Postgres or MySQL, caches, clusters, networks, and CI/CD pipelines.
- What you can do after
- Operate/configure development, staging, and production infrastructure for data-intensive applications and LLM deployments.
Related concepts
- deployment pipelineincludes · A sequence of automated or manual stages that builds, tests or validates, packages, and delivers software or models toward deployment.
- GenAIOpsused by · GenAIOps is the operational discipline of applying DevOps practices to develop, deploy, monitor, and maintain generative AI applications.
- APIOpsuses · APIOps is the practice of automating the governance, delivery, and operation of API products across their life cycle.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.