Docs

This document explores the evolution from monolithic to microservices architectures, highlighting their differences, challenges, and best practices for building, scaling, and communicating between services.

This document introduces the essential concepts and building blocks of system design, including client-server architecture, DNS, proxies, HTTP/HTTPS, APIs databases, and scaling strategies for modern applications.

This lab guides you through pulling an image from Docker Hub and running it as a container. It is a simple exercise to familiarize you with Docker commands and the process of working with container images.

Explains the concept, process, and best practices of service binding in Kubernetes, including how to securely connect applications to external services using secrets and environment variables.

Explains Kubernetes ConfigMaps and Secrets, their characteristics, creation methods, and usage for managing configuration and sensitive data in deployments.

This document explains the concept, process, and best practices of rolling updates in Kubernetes, ensuring zero downtime and safe application upgrades.

This document explains Kubernetes autoscaling, including HPA, VPA, and CA their mechanisms, configuration, and best practices for optimizing resource usage and cost. It covers how each autoscaler works and when to use them.

This document provides an overview of various container orchestration tools their features, and use cases. It covers popular tools like Kubernetes, Docker Swarm, and Apache Mesos, highlighting their strengths and ideal scenarios for deployment.

This document explains ReplicaSet in Kubernetes, its role in maintaining desired pod states, scaling, redundancy, and best practices for deployment management. It covers how ReplicaSets work, their benefits, and practical usage examples.

This document provides a step-by-step guide for setting up a local Kubernetes lab using MicroK8s on Ubuntu 24.04. It includes instructions for installing necessary components, enabling add-ons like DNS and Helm, deploying sample applications, and accessing the Kubernetes Dashboard. It also outlines file organization and usage of kubectl.

This document outlines the essential prerequisites and foundational concepts needed before learning Kubernetes, including containerization, cloud basics YAML, networking, and terminal proficiency.

This document explains kubectl, the Kubernetes command-line tool, its command structure, types of commands, and best practices for managing cluster resources.

This document provides an overview of the Kubernetes architecture, including its components and how they interact to manage containerized applications.

This document explains Kubernetes services, their types, and related objects like Ingress, DaemonSet, StatefulSet, and Job, focusing on their roles in application networking and management.

This document explains Kubernetes objects, their properties, types, and relationships, including how they are defined, managed, and grouped using labels and namespaces.

Overview of Kubernetes, its core concepts, capabilities, and ecosystem. Explains what Kubernetes is and is not, its role in container orchestration and how it automates deployment, scaling, and management of containerized applications.

Comprehensive overview of generative AI tools for code generation, including capabilities, strengths, limitations, and key platforms such as ChatGPT Gemini, Copilot, Polycoder, Watson Code Assistant, Amazon CodeWhisperer, Tab9 and Repl.it. Covers productivity, best practices, and ethical considerations.

Overview of generative AI tools for audio and video, including speech generation, music creation, audio enhancement, and video synthesis. Covers key platforms, capabilities, and real-world applications in creative and professional domains.

This document covers the wide-ranging applications of Generative AI across various domains such as content creation, drug discovery, engineering finance and healthcare.

This document traces the evolution of Generative AI, from early rule-based systems in the 1950s to the latest advancements in Large Language Models (LLMs), highlighting key milestones like GANs and Transformers.