DevOps Training & Certification

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

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

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

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

Topics

1. Introduction to DevOps Practices & Tools

Defining DevOps: Core principles (Culture, Automation, Lean, Measurement, Sharing)

Benefits of DevOps adoption

Overview of key toolchains across the software development lifecycle

Setting the context for the course.

2. AWS Account & Server Setup

Guidance on creating an AWS free tier account

Navigating the AWS Management Console

Launching and connecting to a basic EC2 Linux instance for practical exercises.

3. AZURE Account & Server Setup

Guidance on setting up an Azure free account

Navigating the Azure Portal

Provisioning and connecting to a basic Linux Virtual Machine for practical exercises.

Topics

1. Introduction to Linux OS

Exploring the fundamentals of the Linux operating system.

2. Linux Distributions and Architecture

Understanding different distributions and the architecture of Linux.

Command Line Interface (CLI) & Filesystem

Mastering the CLI and navigating the Linux filesystem.

3. File Management and vi Editor

Managing files and editing them using the vi editor.

4. Archives and Package Management

Utilizing tar, zip utilities, and managing packages in Linux.

5. System Installation and Package Managers

Installing software on Ubuntu, using .deb files, and the APT package manager.

6. Users, Groups, and Permissions

Managing users and groups, and configuring permissions.

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

Networking Basics: IP Address, Protocols, & Ports

8. Firewalls and Security Measures

Configuring firewalls and understanding basic security measures.

9. Load Balancers

Basics of load balancing in a Linux environment for optimizing performance and reliability

Topics

1. Introduction to Version Control System

Basics of version control and its importance in software development.

2. Centralised vs Distributed Version Control System

Differences between centralized and distributed systems, with a focus on their advantages and use cases.

3. Git & GitHub Introduction

Overview of Git and GitHub, and how they revolutionize code management and collaboration.

4. Git Workflow

Understanding the standard workflow in Git, including stages of code changes and commit practices.

5. GitHub for Collaboration

Using GitHub features for project collaboration, including issues, forks, and pull requests.

6. Git Branching Model

Strategies for branching in Git, including feature branches and the master branch.

7. Git Merging and Pull Requests

Techniques for merging branches and the role of pull requests in collaborative projects.

8. Git Rebase

Understanding rebase, its advantages, and how it differs from merging.

9. Handling Detached Head and Undoing Changes

Managing a detached HEAD in Git and various ways to undo changes.

10. Advanced Git Features: Git Ignore, Tagging

Utilizing .gitignore for excluding files from tracking, and tagging for marking specific points in history.

Topics

1. Introduction to Containerisation

Essentials of container technology and its impact on software development.

2. Monolithic vs Microservices Architecture

Comparison between traditional monolithic and modern microservices approaches.

3. Introduction to Virtualisation and Containerisation

Basic concepts of virtualisation and how containerisation offers streamlined deployment.

4. Docker Architecture

Key components and structure of Docker’s system architecture.

5. Setting up Docker

Guidelines for Docker installation and initial setup on various platforms.

6. Docker Registry, Images, and Containers

The roles and relationships between Docker Registry, images, and containers.

7. Running Docker Containers

Fundamentals of managing Docker containers’ lifecycle.

8. Docker Volumes and Networks

Using Docker volumes for data persistence and networks for inter-container communication.

9. Docker Logs and Tags

Techniques for handling Docker logs and utilizing tags for image management.

10. Dockerize Applications and Docker Compose

Strategies for containerizing applications and orchestrating with Docker Compose.

Topics

1. CI/CD Introduction

Definition of Continuous Integration (CI) and Continuous Deployment/Delivery (CD).

Benefits: faster feedback loops, automated testing processes, and dependable releases.

Typical CI/CD workflow outline.

2. GitHub Actions Workflows

Introduction to GitHub Actions as an integrated CI/CD solution within GitHub.

Core concepts: workflows, events, jobs, steps, actions, runners explained.

Creation of fundamental workflow YAML files.

3. Triggers & Runners

Configuration of workflow triggers based on GitHub events (e.g., `push`, `pull_request`, `schedule`).

Distinction between GitHub-hosted and self-hosted runners for workflow job execution.

4. Jobs

Definition of jobs within a workflow for task organization.

Understanding job dependencies and execution environments. Structuring steps within a job.

5. CI/CD Pipeline

Practical construction of a complete CI/CD pipeline using GitHub Actions.

Example: Automated build, test, and potential deployment of a basic application upon code push to the repository.

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.

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

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 AWS EKS

Introduction to Amazon Elastic Kubernetes Service (EKS).

3. Setup EKS

Steps for setting up a Kubernetes cluster on AWS EKS.

4. Deploy Applications On EKS

Practical guide to deploying applications on EKS.

5. Monitoring and Logging

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

This module provides an overview of Azure DevOps, including its core services and how to start with pipelines.

Topics

1. What is Azure DevOps?

An overview of Azure DevOps services and its ecosystem.

2. Azure Boards

Introduction to project management using Azure Boards.

3. Azure Repos

Managing code repositories with Azure Repos.

4. Azure Pipelines

Automating builds, tests, and deployments with Azure Pipelines.

5. Creating Pipelines in Azure DevOps

Step-by-step guide to setting up your first pipeline.

Topics

1. Agile Project Management Best Practices

Implementing agile methodologies using Azure Boards.

2. Basic Concepts of Azure Boards

Understanding work items, sprints, and scrum features.

3. Connecting Boards to GitHub

Integrating Azure Boards with GitHub repositories.

4. Work Items and Sprints

Managing tasks and sprints in Azure Boards for agile development.

5. Azure Boards Integrations

Enhancing Azure Boards with integrations for extended functionalities.

Topics

1. Introduction to Azure Repos

Overview and key concepts of using Azure Repos for source control.

2. Branches and Cloning in Azure Repos

Managing branches and cloning repositories for development workflows.

3. Import Code from GitHub

Steps to import existing codebases from GitHub into Azure Repos.

4. Search Your Code in Repos

Utilising search functionalities within Azure Repos for code management.

5. Azure Repos Integrations

Extending Azure Repos capabilities with external integrations.

Topics

1. Deploying with Azure Pipelines

Strategies for deploying applications using Azure Pipelines.

2. CI Triggers and YAML Basics

Configuring continuous integration triggers and understanding YAML for pipeline configuration.

3. Setting Up CI Build

Creating a continuous integration build process with Azure Pipelines.

4. Adding Tests to the Pipeline

Incorporating testing into the CI/CD pipeline for quality assurance.

5. Agents and Tasks

Understanding agents and tasks within Azure Pipelines for build and deployment processes.

Topics

1. Working with Packages in Azure Artifacts

Managing dependencies and packages with Azure Artifacts.

2. Connection Feeds and Views in Artifacts

Configuring feeds for package sharing and views for package management.

3. Connecting Azure Artifacts to Azure Pipelines

Automating package deployment with Azure Pipelines integration.

4. What are Azure Test Plans?

Introduction to planning, executing, and tracking tests with Azure Test Plans.

5. Testing Web Apps

Strategies and best practices for testing web applications using Azure Test Plans.

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.

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.

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