What is the DevOps life cycle? Explanation with a real Modsen project example

What is DevOps software development life cycle?

DevOps life cycle phases, explained through Modsen project

There are 8 phases of DevOps life cycle: plan, code, build, test, release, deploy, operate and monitor. 

To illustrate how DevOps software development life cycle works in projects, let’s look at a project delivered by Modsen – an AI-powered HR chatbot. AI systems change constantly, so DevOps alone is not enough – they also need MLOps for model updates and AIOps for intelligent monitoring.

Just for context:

According to Google. MLOps: Continuous delivery and automation pipelines in ML; Gartner. AIOps Platforms Market Guide (2022). 

Below, I break down and explain DevOps life cycle throughout this project.

Plan: Understanding HR workflows and defining AI architecture

In the HR chatbot project, we didn’t start by coding.  We began by aligning HR specialists, developers, QA, DevOps engineers (Who is DevOps engineer? Read here), and security experts on:

• what types of HR questions the chatbot must handle,

• what rules and policies its answers must follow,

• how sensitive employee data must be protected (GDPR),

• and what architecture allows the system to handle more users without breaking safely.

Code: Designing AI and backend modules

The chatbot’s backend and AI model were written using strict rules: clean structure, documented API (how systems talk to each other), version control (system for tracking changes), mandatory code review.

How it works for this project:

• Clean, well-documented code following internal standards.

• Git flow that avoids conflicts and mistakes.

• Mandatory code review for quality and security.

• Clear API design and versioning from the start.

Build: CI and automated ML pipeline assembly

Every change to the chatbot triggered an automatic build:

• CI (continuous integration) pipeline checks dependencies,

• unit tests run,

• Back end and AI components are packaged into containers (lightweight isolated environments for running code).

DevOps replaces manual work with automation, so the system assembles itself.

Test: Ensuring chatbot correctness and stability

For the HR chatbot, we tested:

• Natural language processing (NLP) accuracy (does the model understand HR requests?),

• Back end logic,

• security and access rules,

• integrations with HR systems.

Release: Ensuring model quality and compliance

Before shipping a new version of the chatbot, we ran a release checklist:

• model accuracy audit,

• GDPR compliance verification,

• performance and latency checks (the time it takes for the system to respond),

• HR validation session (a final HR check of chatbot answers).

Deploy: Zero-downtime deployment of chatbot

The chatbot was deployed via:

• automated  CI/CD pipelines,

• Kubernetes (traffic cop for the containers), handling scaling and stability,

• Blue-green deployment (two identical versions of the app – one live, one updated), preventing downtime.

Operate: Maintaining a reliable AI system

Once the project went live, our DevOps team ensured:

• stable infrastructure,

• proper configuration,

• 24/7 availability,

• smooth scaling when user activity increased.

Monitor: Continuous tracking of model accuracy and system health

After deployment, we continuously monitored:

• model accuracy,

• chatbot fallback rate,

• employee satisfaction signals,

• system metrics (CPU, memory, errors).

These insights informed the next improvement cycle: monitoring → new ideas → new improvements → new plan.

That is, DevOps software development process, in which one team is responsible for everything: from the idea to the system working in the real world, and back to improving the idea.

Source: The Modern DevOps Lifecycle (2023); The DevOps Handbook (2021). 

The 7Cs of DevOps life cycle

Continuous development

This stage covers ongoing planning and coding. Instead of working on one large feature for months, the team breaks the project into small, manageable updates that can be delivered quickly.

For the project, engineers continuously refined intents, HR logic, and NLP components as the chatbot learned from new queries.

Continuous integration

Every time developers commit code, the system automatically checks it, tests it, and integrates it with the rest of the project. This prevents conflicts and catches issues early.

For the project, each update to the chatbot’s backend or AI pipeline triggered automated tests and builds, ensuring the codebase stays stable.

Continuous testing

Tests run automatically throughout the entire DevOps software development life cycle, not only before release. This helps validate new code and detect regressions.

For the project, we continuously tested NLP accuracy, HR policy compliance, and conversation flows.

Continuous deployment

When code passes all tests, it can be deployed automatically to staging or production environments. No manual steps, no delays, no downtime.

For the project, updates to the chatbot were deployed through CI/CD pipelines, using blue-green deployments to avoid service interruption.

Continuous monitoring

The team constantly tracks performance, errors, system health, and user behavior. Monitoring during DevOps life cycle helps detect problems early and maintain high reliability.

For the project, we monitored inference latency, model accuracy, fallback rates, and system load to ensure the chatbot remained stable and responsive.

Continuous feedback

Feedback comes from real users, logs, HR teams, and monitoring systems. It’s used to improve the model, update conversation flows, and refine system behavior.

For the project, HR specialists’ feedback and real employee dialogues helped improve the NLP model and update HR response logic.

Continuous operations

This ensures the system keeps running smoothly without taking it offline for updates or maintenance. Everything – scaling, patching, rollouts – is designed to be seamless.

For the project, Kubernetes handled scaling, redundancy, and self-healing, allowing the HR chatbot to stay available 24/7 with no downtime.

DevOps life cycle tools & technologies

None of the phases or 7Cs can be achieved manually. That’s why DevOps SDLC life cycle is always supported by a stack of tools that handle all in a consistent way.

In the AI HR chatbot project, these tools were the backbone of our workflow: they allowed us to automate builds, run ML tests, deploy updates without downtime, monitor system performance, and collect feedback for retraining the model.

What is the take?

The DevOps life cycle is based on continuity – a steady, repeatable rhythm that keeps products evolving instead of decaying. When each phase feeds the next, teams stop fixing the same issues over and over again and start building software that grows stronger with every iteration. It’s this cycle that turns ideas into systems that stay maintainable, scalable, and reliable long after launch.

And if these notes made you wonder, what are the skills required for a DevOps engineer to orchestrate a life cycle like this, we’ve outlined the essentials in a dedicated article – all the requirements which will make you a real expert.