Site Reliability Engineering
SRE Training

1. What is an Application?

2. Types of Applications

3. Web Application Fundamentals

4. Web Technologies: (List key technologies and their roles)

Frontend: HTML, CSS, JavaScript, React

Backend: Python, Java, Node.js

Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

5. Software Development Life Cycle (SDLC)

Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

6. Application Development Methodologies

Agile: Core principles, Scrum, Kanban

Waterfall

1. What is Data

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

1. The Importance of Computing Power

2. Key Computing Technologies:

CPU (Central Processing Unit)

GPU (Graphics Processing Unit)

3. Cloud Computing:

What is the Cloud?

Cloud Service Models:

IaaS (Infrastructure as a Service)

PaaS (Platform as a Service)

SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)?

2. How AI Works?

3. Key Concepts:

Machine Learning (ML)

Deep Learning (DL)

4. Generative AI:

What is Generative AI?

Examples: Large Language Models (LLMs), image generation models.

5. AI in Everyday Learning

1. Customer Relationship Management (CRM)

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

Topics:

1. Introduction to Linux OS

Exploring the fundamentals of the Linux operating system and its importance in DevOps environments.

2. Linux Distributions and Architecture

Understanding various Linux distributions and the architecture of Linux-based systems.

3. Command Line Interface (CLI) & Filesystem

Mastering the CLI and understanding how to navigate and manage the Linux filesystem.

4. File Management and vi Editor

Techniques for managing files in Linux, including file manipulation and editing with vi.

5. Archives and Package Management

Utilizing tools like tar and zip for file archiving and managing packages in Linux.

6. System Installation and Package Managers

Installing and managing software using package managers such as APT and YUM.

7. Users, Groups, and Permissions

Managing users and groups, and configuring file and system permissions to maintain security.

8. Networking Basics: IP Address, Protocols, & Ports

Understanding basic networking concepts like IP addressing, protocols, and port management in Linux.

9. Firewalls and Security Measures

Configuring firewalls and implementing security best practices to protect Linux-based systems.

10. Load Balancers

Introduction to load balancing techniques in Linux environments for improving scalability and reliability.

Topics:

1. Introduction to Version Control System

Basics of version control systems and their role in managing software code and collaboration.

2. Centralized vs Distributed Version Control Systems

Exploring the differences between centralized and distributed version control systems with practical examples.

3. Git & GitHub Introduction

Overview of Git as a distributed version control system and GitHub as a platform for hosting and collaborating on Git repositories.

4. Git Workflow

Understanding the typical workflow in Git, including stages of code changes, commits, and push/pull operations.

5. GitHub for Collaboration

Using GitHub for effective collaboration in teams, including issues, pull requests, and project boards.

6. Git Branching Model

Strategies for managing different branches in Git, including feature branches, master/main, and release branches.

7. Git Merging and Pull Requests

Techniques for merging code and using pull requests for collaborative code review and integration.

8. Git Rebase

A deep dive into Git rebase, its advantages, and how it improves the Git history.

9. Handling Detached Head and Undoing Changes

Best practices for managing detached HEAD states in Git and methods to undo changes or revert commits.

10. Advanced Git Features: Git Ignore, Tagging

Leveraging `.gitignore` for excluding unwanted files from version control and tagging releases for version management.

Topics:

Topics: Introduction to SonarQube Purpose and benefits of using SonarQube in software development. Core Features Static Code Analysis: Identifies bugs, vulnerabilities, and code smells. Quality Gates: Ensures code meets quality standards. Continuous Integration: Integrates with CI/CD pipelines for automated checks. Security Analysis: Highlights security vulnerabilities. Setup and Use Installation steps. Running initial code analysis and interpreting results.

Topics: Introduction to Nexus Repository Purpose and advantages of using Nexus Repository in development environments. Key Features Artifact Storage: Manages libraries, build artifacts, and binaries. Repository Management: Supports multiple repository formats like Maven, NuGet, and Docker. Access Control: Manages user permissions for better security. Installation and Configuration Step-by-step guide for setting up Nexus Repository. Using Nexus Repository Uploading and managing artifacts. Integrating with build tools and CI/CD pipelines. Best Practices Efficient repository organization and version control. Case studies highlighting successful Nexus Repository implementations.

Topics: Introduction to Containerisation Essentials of container technology and its impact on software development. Monolithic vs Microservices Architecture Comparison between traditional monolithic and modern microservices approaches. Introduction to Virtualisation and Containerisation Basic concepts of virtualisation and how containerisation offers streamlined deployment. Docker Architecture Key components and structure of Docker's system architecture. Setting up Docker Guidelines for Docker installation and initial setup on various platforms. Docker Registry, Images, and Containers The roles and relationships between Docker Registry, images, and containers. Running Docker Containers Fundamentals of managing Docker containers' lifecycle. Docker Volumes and Networks Using Docker volumes for data persistence and networks for inter-container communication. Docker Logs and Tags Techniques for handling Docker logs and utilizing tags for image management. Dockerize Applications and Docker Compose Strategies for containerizing applications and orchestrating with Docker Compose.

Topics: Introduction to CI/CD & Jenkins Combined basics of Continuous Integration, Continuous Deployment, and the role of Jenkins. Benefits and Requirements of CI/CD Key advantages of adopting CI/CD and the foundational requirements for successful implementation. Setting Up Jenkins Initial setup and configuration of Jenkins for CI/CD pipelines. Build Tools and Repository Management Overview of essential build tools and repository managers integrated with Jenkins for CI. Jenkins Job Configuration Creating and configuring jobs in Jenkins for automated builds. Automating with Build Triggers Using build triggers in Jenkins for automatic job execution based on specific conditions. Jenkins Pipelines and Pipeline as Code Developing robust CI/CD workflows using Jenkins Pipelines and defining them through code. Utilizing Jenkins Plugins Enhancing Jenkins functionalities with plugins for broader CI/CD capabilities. Continuous Deployment with Jenkins Implementing continuous deployment practices using Jenkins to streamline software releases.

Topics:

1. Introduction to High Availability

Understanding the importance of high availability in systems design.

2. Introduction to Container Orchestration

Exploring the concept and need for container orchestration.

3. Container Orchestration Tools

Overview of tools available for container orchestration including Kubernetes.

4. Overview of Kubernetes

Introduction to Kubernetes and its role in container orchestration.

5. Kubernetes Architecture

Understanding the architectural components of Kubernetes.

Topics:

1. Components of Kubernetes

Detailed look at core Kubernetes components, including master and node components.

2. Kubernetes Objects

Introduction to the fundamental objects in Kubernetes.

3. Pods

Understanding Pods, the smallest deployable units in Kubernetes.

4. Replica Sets

Role and functioning of Replica Sets in managing pods.

5. Deployments

How Deployments automate the updating and rollback of applications.

Topics:

1. Services

Introduction to Services as a way to expose applications running on a set of Pods. 2. ClusterIP

Exploring ClusterIP for internal cluster communication.

3. NodePort

Understanding how NodePort exposes services outside of the cluster.

4. Load Balancer

Using Load Balancers to distribute traffic evenly across services.

5. Ingress

Configuring Ingress for external access to services within the cluster.

Topics:

1. Config Maps

Managing application configuration using Config Maps.

2. Secrets

Securely storing sensitive information with Secrets.

3. Persistent Volume (PV) and Persistent Volume Claim (PVC)

Understanding the storage capabilities in Kubernetes with PV and PVC.

4. Storage Classes

Exploring dynamic volume provisioning through Storage Classes.

5. StatefulSets

Managing stateful applications with StatefulSets.

Topics:

1. Overview of Production Clusters

Considerations for running Kubernetes in production environments.

2. Overview of AKS

Introduction to Azure Kubernetes Service (AKS).

3. Setup AKS

Steps for setting up a Kubernetes cluster on AKS.

4. Deploy Applications On AKS

Practical guide to deploying applications on AKS.

5. Monitoring and Logging

Tools and strategies for monitoring and logging in a Kubernetes environment.

Topics

1. Introduction to SRE

Defining Site Reliability Engineering and its objectives in maintaining highly reliable and scalable systems.

2. Introduction to Monitoring

Exploring the purpose and techniques of monitoring in SRE practices.

3. Introduction to Observability

Understanding observability and its difference from and relationship with monitoring.

4. SRE Roles and Responsibilities

Overview of the typical roles, responsibilities, and expectations of an SRE.

5. SRE Best Practices and Principles

Essential practices and foundational principles for effective site reliability engineering.

Topics

1. Introduction to Prometheus

Basics of Prometheus and its role in the monitoring landscape.

2. Prometheus Architecture

Understanding the components and architecture of Prometheus.

3. Monitoring with Prometheus

Setting up Prometheus for monitoring infrastructure and application metrics.

4. Scraping Metrics with Prometheus

Techniques for scraping and collecting metrics from various targets.

5. Prometheus YAML Configs and Node Exporter

Configuring Prometheus and using Node Exporter to gather system metrics.

Focuses on Grafana for visualizing metrics and logs, providing insights into creating effective dashboards for observability.

Topics

1. Introduction to Visualization with Grafana

Understanding the importance of data visualization in observability.

2. Installing Grafana on a Linux Server

Step-by-step installation of Grafana for setting up monitoring dashboards.

3. Grafana User Interface Overview

Navigating through Grafana's UI and understanding its features.

4. Creating Grafana Dashboards

Techniques for creating insightful and interactive dashboards in Grafana.

5. Grafana with Docker

Deploying Grafana within Docker containers for flexible and scalable monitoring solutions.

Topics

1. Integrating Prometheus and Grafana

Techniques for integrating Prometheus with Grafana to visualize metrics.

2. Alerting with Prometheus

Setting up alert rules in Prometheus and integrating with notification platforms.

3. Log Management and Analysis

Introduction to log management solutions and integrating them with monitoring tools for full observability.

4. Tracing and Distributed Tracing

Understanding tracing and distributed tracing for in-depth insights into application performance.

5. Cloud Monitoring Solutions

Overview of cloud-native monitoring and observability solutions provided by cloud service providers.

Topics

1. Infrastructure as Code (IaC) for SRE

Leveraging IaC tools for reliable and reproducible infrastructure provisioning.

2. CI/CD Pipelines for Reliable Deployments

Implementing CI/CD pipelines for automated testing and deployment.

3. SRE and DevOps: Collaboration and Tools

Exploring the overlap between SRE and DevOps practices, focusing on tooling and collaboration for reliability.

4. Automation in Incident Management

Automating incident response and management to reduce downtime and improve MTTR (Mean Time To Recovery).

5. Capacity Planning and Performance Tuning

Techniques and tools for effective capacity planning and performance tuning to ensure scalability and reliability.

Topics

1. Python as a Scripting Language

Overview of Python and its use as a powerful scripting language.

2. Python Collections and Sequences

Introduction to Python's data structures for organizing and storing data.

3. Working with Python Collections

Practical exercises on manipulating lists, dictionaries, sets, and tuples.

4. Python Functional Programming

Understanding functional programming paradigms in Python, including lambda functions and higher-order functions.

5. Python File Handling

Techniques for reading from and writing to files in Python scripts.

Topics

1. Python Modules and Packages

Utilizing modules and packages to organize and reuse code efficiently.

2. Classes in Python

Fundamentals of defining and using classes in Python.

3. Object-Oriented Programming (OOP) in Python

Exploring Python's OOP features for more complex script development.

4. Exception Handling

Techniques for handling and raising exceptions to manage errors gracefully.

5. Python Decorators and Generators

Leveraging decorators and generators to simplify and power up your Python code.

Topics

1. Automation through Scripting Languages

The role of scripting languages like Python in automation efforts.

2. Automating File System Operations

Using Python scripts to manage file and directory operations.

3. Web Scraping with Python

Techniques for extracting data from web pages using Python libraries.

4. Automating Network Tasks

Scripting network operations for automation with Python.

5. Automating API Interactions

Using Python to interact with and automate tasks using APIs.

Topics

1. Building Python Applications

Best practices and methodologies for developing robust Python applications.

2. Testing Python Applications

Introduction to unit testing and test automation in Python.

3. Python Application Deployment

Strategies for deploying Python applications, including web and standalone applications.

4. CI/CD for Python Applications

Implementing Continuous Integration and Continuous Deployment workflows for Python projects.

5. Virtual Environments and Package Management

Managing Python environments and dependencies for project isolation and reproducibility.

Topics

1. Python in CI/CD Pipelines

Integrating Python scripts and applications in CI/CD workflows.

2. Automating Builds and Tests with Python

Using Python for automated testing, including unit tests, integration tests, and end-to-end tests.

3. Python for Deployment Automation

Scripting deployment processes, including application packaging and distribution.

4. Monitoring and Logging with Python

Implementing monitoring and logging solutions in Python for applications and infrastructure.

5. Version Control Automation with Python

Automating version control workflows with Git using Python.

Introduction to Generative AI

1. What is Generative AI?

2. Key Applications:

Text (ChatGPT, Claude, LLaMA)

Images (DALL·E, MidJourney, Stable Diffusion)

Audio (Music Generation, Voice Cloning)

Code (GitHub Copilot, Cursor)

3. Evolution of GenAI:

Rule-Based → Deep Learning → Transformers

GANs vs. VAEs vs. LLMs

1. Effective Prompt Design

Instruction-Based, Few-Shot, Zero-Shot

2. Advanced Techniques:

Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Hands-on:

Optimizing prompts for GPT-4, Claude, LLaMA

Transformer Architecture

1. Why Transformers? (Limitations of RNNs/LSTMs)

2. Key Components:

Self-Attention & Multi-Head Attention

Encoder-Decoder (BERT vs. GPT)

3. Evolution: BERT → GPT → T5 → Mixture of Experts

4. Large Language Models (LLMs)

5. Pre-training vs. Fine-tuning

6. Popular Architectures:

GPT-4, Claude, Gemini, LLaMA 3

BERT (Encoder-based) vs. T5 (Text-to-Text

Introduction to AI Agents

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

CrewAI + n8n: Automating Business Workflows

Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

Case Studies:

AI Customer Support Agents