What is DevOps, Prerequisites, DevOps with AI
Source: Dev.to
DevOps Overview
DevOps is a set of practices, tools, and a cultural philosophy that automates and integrates the processes between software development (Dev) and IT operations (Ops).
Its primary goal is to shorten the systems development life‑cycle and provide continuous delivery with high software quality. Instead of these two teams working in silos (isolation), they collaborate across the entire service lifecycle.
Stages of DevOps
The DevOps lifecycle is often represented as an infinity loop, symbolizing continuous improvement.
| Stage | Description | Typical Tools |
|---|---|---|
| Plan | Define business value and requirements. | Jira, Trello |
| Code | Write code and manage changes with a VCS. | Git |
| Build | Compile and package code into an executable format. | Maven, Gradle |
| Test | Run automated tests (unit, integration, performance) to catch bugs early (“Fail Fast”). | JUnit, Selenium, Gatling |
| Release | Prepare the build for deployment; schedule and manage versions. | Release pipelines in Azure DevOps, GitLab |
| Deploy | Push software to production servers using IaC to ensure consistent environments. | Terraform, Ansible |
| Operate | Manage configuration and performance of the live application. | Docker, Kubernetes |
| Monitor | Continuously track system health and user behavior. | Nagios, Splunk, Prometheus |
How to Adopt a DevOps Model
Adopting DevOps is as much about culture as it is about tools. Below is a step‑by‑step roadmap.
- Change the Culture – Shift from “blame” to “shared responsibility.” Developers and Ops must align on common goals (e.g., system uptime is everyone’s job).
- Adopt Agile Methodologies – Move from long, rigid planning cycles to short, iterative sprints (Scrum/Kanban) to deliver work in small chunks.
- Implement CI/CD
- CI – Automate code merging and testing.
- CD – Automate release to staging/production environments.
- Automate Infrastructure (IaC) – Stop manually configuring servers. Use code scripts (e.g., Terraform) to provision environments, eliminating “it works on my machine” errors.
- Automate Testing – Replace manual QA with automated test scripts that run on every code change.
- Establish Continuous Monitoring – Set up real‑time dashboards to view system health and react to crashes instantly.
How DevOps Saves Costs (Real‑World Examples)
DevOps reduces costs by eliminating waste—time, resources, and missed opportunities.
| Benefit | Why It Saves Money |
|---|---|
| Less Downtime | Automated monitoring catches bugs before they crash the system; downtime = lost sales. |
| Faster Time‑to‑Market | Releasing features in weeks instead of months means revenue starts flowing sooner. |
| Lower Personnel Costs | Automation handles repetitive tasks (e.g., server updates), freeing senior engineers to focus on innovation. |
Real‑Time Examples
Example 1 – Network Rail (UK)
Problem: Legacy testing environment was slow; releases required manual intervention and caused days of downtime.
DevOps Solution: Adopted Infrastructure as Code and automated testing.
Cost Impact: Configuration time dropped from 5.5 days to minutes, saving massive operational man‑hours and avoiding penalty fees.
Example 2 – Target (Retail Giant)
Problem: After a 2013 data breach, Target’s monolithic update process made security patching difficult.
DevOps Solution: Implemented a DevSecOps model, integrating security into CI/CD pipelines; moved from quarterly releases to thousands of releases per day.
Cost Impact: Automated security checks helped avoid potentially billion‑dollar breach costs and reduced change‑failure rates, saving millions in remediation.
DevOps vs. Waterfall
| Feature | Waterfall Model | DevOps Model |
|---|---|---|
| Process Type | Linear and sequential (Step A must finish before Step B). | Cyclic and iterative (continuous loop of planning, coding, testing). |
| Collaboration | Siloed – developers hand off code to testers, then to ops. | Collaborative – dev, QA, and ops work together from the start. |
| Feedback Loop | Slow – you only discover problems at the very end. | Fast – immediate feedback after every commit. |
| Release Cycle | Long (months/years) – “big‑bang” releases. | Short (daily, weekly, or bi‑weekly) – continuous micro‑releases. |
| Risk | High – fixing bugs at the end is expensive and delays launch. | Low – bugs are found and fixed immediately in small batches. |
| Focus | Process adherence and strict planning. | Speed of delivery and business value. |
Prerequisites Before Learning DevOps
DevOps is not an entry‑level IT skill; it sits at the intersection of Development and Operations. To succeed, you need a solid foundation in the technologies that DevOps tools rely on.
1. Linux Operating System (The Foundation)
- Why? ~90 % of DevOps infrastructure runs on Linux; GUIs are rarely used.
- What to Learn:
- CLI Basics:
cd,ls,pwd,cp,mv,rm - Permissions:
chmod,chown,sudo - Text Editing:
vim,nano - Package Management:
apt‑get,yum,dnf
- CLI Basics:
2. Networking Basics (The Plumbing)
- Understand how applications communicate across servers (TCP/UDP, DNS, load balancing, firewalls, VPNs, etc.).
3. Scripting & Automation (The Glue)
- Bash, Python, or PowerShell for writing reusable automation scripts.
4. Version Control (The Backbone)
- Git fundamentals: branching, merging, rebasing, pull requests, and tagging.
Once you’re comfortable with these four pillars, you’ll be ready to dive into tools like Docker, Kubernetes, Jenkins, and other components of the modern DevOps toolbox.
1. Networking Basics
If you don’t understand networking, you cannot troubleshoot deployment failures.
What to learn
- IP Addresses & Ports – e.g., why a web server runs on port 80/443.
- DNS – how a domain name resolves to an IP address.
- HTTP/HTTPS – common status codes (200 OK, 404 Not Found, 500 Server Error).
- SSH – how to securely log in to remote servers.
2. Basic Scripting (The Glue)
You don’t need to be a full‑stack developer, but you must know how to write scripts to automate repetitive tasks.
What to learn
| Language / Format | Why it matters |
|---|---|
| Bash / Shell Scripting | Native language of Linux. |
| YAML & JSON | Data‑serialization formats used by almost all DevOps tools (Kubernetes, Ansible, Docker Compose). |
| Python (good to have) | Enables more complex automation logic. |
3. SDLC & Git (The Process)
DevOps is about speeding up the Software Development Life Cycle (SDLC).
What to learn
- Git – understand
git clone,git commit,git push, and how to resolve merge conflicts. - Agile – grasp sprints and iterative development.
4. Who Can Learn DevOps?
DevOps is a methodology, not a specific degree. Most DevOps Engineers transition from other roles.
| Current Role | Ease of Transition | What You Need to Focus On |
|---|---|---|
| System Administrators (Ops) | High | You already know Linux and networking. Focus on automation (Python/Bash) and cloud (AWS/Azure). |
| Software Developers (Dev) | High | You already know code and Git. Focus on Linux, networking, and infrastructure management. |
| QA / Test Engineers | Medium | You already know the release process. Focus on automated testing and CI/CD pipelines (Jenkins/GitLab). |
| Fresh Graduates (CS/IT) | Medium | You have the theory. Focus heavily on hands‑on labs; university curricula rarely cover real‑world DevOps tools. |
| Non‑IT Background | Hard | Possible, but you have a longer road. Spend 1‑2 months strictly learning Linux and networking first. |
5. DevOps + AI
The intersection of DevOps and AI creates a powerful two‑way relationship. It can be confusing because it refers to two distinct concepts:
| Concept | Description |
|---|---|
| AI for DevOps (AIOps) | Using AI tools to improve the DevOps workflow (e.g., AI detecting bugs). |
| DevOps for AI (MLOps) | Using DevOps principles to manage AI development (e.g., version control for AI models). |
5.1 AI for DevOps (AIOps)
Applying artificial intelligence to the DevOps pipeline makes it faster, smarter, and more automated.
- Smart Code Reviews – AI tools (Amazon CodeGuru, DeepCode) scan code for bugs, security vulnerabilities, and logic errors in real‑time.
- Predictive Monitoring – AI‑driven monitoring (Dynatrace, Datadog) analyzes historical data to predict failures before they happen (e.g., “Memory usage is trending up; server will fail in 2 hours”).
- Automated Incident Response – AI agents can automatically roll back a deployment or restart a service when an error occurs.
- Self‑Healing Infrastructure – AI can automatically scale resources up or down based on predicted traffic, rather than merely reacting to current load.
5.2 DevOps for AI (MLOps)
Building AI models is experimental and messy. MLOps applies the discipline of DevOps (CI/CD, version control) to data science.
- Version Control for Data – In MLOps you version code + data + model parameters. If you retrain a model today, you must be able to reproduce the exact dataset used six months ago.
- Continuous Training (CT) – AI models “drift” over time as real‑world data changes. MLOps pipelines automatically trigger retraining when model accuracy drops.
- Model Registry – Just as you store compiled binaries in an artifact repository (e.g., Nexus), MLOps stores trained models in a model registry before deploying them to production.
5.3 Comparison: DevOps vs. MLOps vs. AIOps
| Feature | DevOps | MLOps (DevOps for AI) | AIOps (AI for DevOps) |
|---|---|---|---|
| Primary Goal | Shorten SDLC & deliver high‑quality software | Reliable & scalable AI models | Automate IT operations |
| Core Artifact | Software application (binary/WAR/JAR) | Machine‑learning model | Incident/alert reports |
| Key Challenge | Bug‑free code | Model drift (accuracy loss) | Noise reduction (too many alerts) |
| New Phase | CI / CD | CI / CD / CT (Continuous Training) | Observe / Engage / Act |
| Example Tool | Jenkins, Docker | Kubeflow, MLflow | Splunk ITSI, BigPanda |
6. Real‑World Use Cases
Case 1 – Netflix (AIOps)
- Challenge: Thousands of microservices generate a “storm” of alerts when a service fails, making root‑cause analysis difficult.
- AI Solution: Netflix uses AIOps to correlate alerts into a single incident, pinpointing the offending service and reducing Mean Time To Resolution (MTTR).
Case 2 – Uber (MLOps)
- Challenge: Uber’s ETA (estimated time of arrival) predictions rely on hundreds of constantly updated models.
- DevOps Solution: Uber built an internal MLOps platform called Michelangelo, allowing data scientists to deploy a model with one click—just like a software engineer deploys code—ensuring the app always uses the most accurate ETA model.