Glossary

A project management approach that breaks work into small, iterative cycles called sprints. Teams deliver working software in short intervals (usually 2 weeks) and adjust priorities based on feedback, rather than following a rigid upfront plan.

A set of rules that lets different software applications talk to each other. When you use a weather app, it pulls data from a weather service through an API. APIs are the glue that connects modern software systems together.

An automated process that tests and deploys code changes quickly and reliably. Instead of manually checking and releasing software every few weeks, CI/CD pipelines catch bugs early and push updates continuously — sometimes dozens of times per day.

One of the most respected cybersecurity certifications in the world, often required for senior security roles. It covers eight domains from risk management to software security and typically requires five years of professional experience to earn.

A hands-on certification that proves you can manage and troubleshoot Kubernetes clusters. Unlike multiple-choice exams, the CKA requires you to solve real problems in a live environment, making it highly valued by employers.

The practice of designing and managing computing infrastructure that runs on cloud platforms like AWS, Azure, or Google Cloud. Cloud architects decide how applications are structured, scaled, and secured in the cloud — replacing the need for physical servers.

The most popular entry-level cybersecurity certification, widely recognized across government and private sectors. It validates foundational security skills like threat detection, risk management, and network security — and is often the first step into a cybersecurity career.

The practice of carefully selecting and structuring the information you feed to an AI model so it produces better, more relevant outputs. Unlike prompt engineering which focuses on phrasing questions well, context engineering is about curating the background knowledge the AI works with.

The discipline of building and maintaining the systems that collect, store, and transform raw data into usable formats. Data engineers create the pipelines and infrastructure that data scientists and analysts rely on — think of them as the plumbers of the data world.

A field that combines statistics, programming, and domain expertise to extract insights and predictions from data. Data scientists build models that can forecast trends, detect fraud, recommend products, and answer complex business questions using large datasets.

A subset of machine learning that uses neural networks with many layers to learn complex patterns from data. Deep learning powers technologies like image recognition, speech-to-text, and language models. It requires significant computing power but achieves remarkable accuracy on complex tasks.

A set of practices that combines software development (Dev) and IT operations (Ops) to deliver software faster and more reliably. DevOps engineers automate building, testing, and deploying code so teams can ship updates quickly without breaking things.

A tool that packages an application and everything it needs to run into a portable container. This means software works the same way on any machine — eliminating the classic 'it works on my computer' problem and making deployment much simpler.

The process of taking a pre-trained AI model and training it further on a specific dataset to improve its performance on a particular task. For example, you might fine-tune a general language model on medical records so it better understands healthcare terminology.

Refers to a developer who works on both the front-end (what users see in the browser) and the back-end (servers, databases, and business logic). Full-stack developers can build complete applications end to end, making them versatile and highly employable.

Managing servers, networks, and cloud resources by writing code instead of clicking through dashboards. IaC makes infrastructure reproducible, version-controlled, and automated — you can spin up identical environments in minutes rather than days of manual setup.

An open-source platform for automating the deployment, scaling, and management of containerized applications. Originally built by Google, Kubernetes (often called K8s) has become the industry standard for running applications at scale across clusters of machines.

An AI model trained on massive amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They power everything from chatbots to code generation and are reshaping how people work across every industry.

A branch of artificial intelligence where systems learn patterns from data instead of being explicitly programmed. Rather than writing rules for every scenario, you feed the system examples and it figures out the patterns — like training it to recognize spam emails by showing it thousands of examples.

An architecture style where an application is built as a collection of small, independent services that communicate over APIs. Each service handles one specific function and can be developed, deployed, and scaled independently — the opposite of a single large codebase (monolith).

The practice of deploying and maintaining machine learning models in production reliably and efficiently. MLOps combines machine learning, DevOps, and data engineering to ensure models don't just work in a notebook — they work in the real world, at scale, and stay accurate over time.

The discipline of building and maintaining internal developer platforms that make it easier for engineering teams to ship software. Platform engineers create self-service tools, templates, and automated workflows so developers can deploy applications without needing to understand all the underlying infrastructure.

The skill of crafting effective instructions for AI models to get the best possible outputs. Prompt engineers design, test, and refine the text inputs that guide AI systems — a crucial skill as companies integrate AI tools into their products and workflows.

A technique that improves AI responses by first searching a knowledge base for relevant information, then feeding that context to the language model. RAG lets AI systems answer questions using up-to-date, company-specific data rather than relying solely on their training data.

A specific framework within Agile that organizes work into fixed-length sprints (typically two weeks). Teams hold daily standups, plan sprints collaboratively, and review completed work at the end of each cycle. Scrum Masters facilitate the process and remove blockers for the team.

A cloud computing model where the provider manages the servers for you. You just write and upload your code — it runs automatically when triggered, scales on demand, and you only pay for actual execution time. AWS Lambda, Google Cloud Functions, and Azure Functions are popular serverless platforms.

A discipline pioneered by Google that applies software engineering practices to IT operations. SREs build automated systems to ensure services stay reliable, fast, and available. They set measurable reliability targets and balance new feature development against system stability.

An open-source Infrastructure as Code tool that lets you define and provision cloud resources using configuration files. Terraform works across all major cloud providers (AWS, Azure, GCP), making it possible to manage your entire infrastructure in a single, version-controlled codebase.

The craft of designing the visual elements users interact with — buttons, icons, typography, colors, and layouts. UI designers ensure that digital products look polished, consistent, and aesthetically pleasing while following established design systems and accessibility standards.

The process of designing products that are easy, enjoyable, and meaningful to use. UX designers research user needs, create wireframes and prototypes, and test designs to ensure the product solves real problems. It's about the entire experience, not just how things look.

A style of programming where you describe what you want in natural language and AI generates the code. Instead of writing syntax from scratch, vibe coders guide AI tools like Cursor, Claude Code, or GitHub Copilot to build applications — focusing on ideas and problem-solving rather than memorizing language specifics.