DevOps & AWS 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 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:

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

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

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

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

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

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

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

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

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

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

Topics:

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

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

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

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

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

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

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

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

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

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

Topics:

The basics of container technology and how Docker revolutionizes software deployment and scalability.

Comparison of traditional monolithic architecture vs modern microservices approaches in application design.

Understanding virtualization and how containerisation offers a more efficient and scalable alternative.

An in-depth exploration of Docker’s architecture and its core components, including Docker daemon, images, and containers.

Guidelines for installing Docker and configuring it on various operating systems and environments.

Exploring Docker images, container creation, and the role of Docker registries for storing and sharing images.

Managing Docker containers, including lifecycle operations such as starting, stopping, and scaling containers.

How to use Docker volumes for persistent storage and Docker networks for inter-container communication.

Handling Docker container logs for troubleshooting and using tags for managing image versions.

Best practices for containerizing applications and orchestrating multi-container applications using 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.

Topics:

What SonarQube is and how it helps in improving code quality by detecting bugs, vulnerabilities, and code smells.

Guide to installing and configuring SonarQube for code quality analysis.

Automating code quality checks by integrating SonarQube with Jenkins or other CI tools.

Understanding the key metrics and quality gates provided by SonarQube to evaluate code quality.

Using SonarQube to track code coverage and report on test results to ensure high test reliability.

How SonarQube identifies security vulnerabilities and issues in the codebase, and best practices for remediation.

Leveraging SonarQube's refactoring recommendations to improve the structure and maintainability of your code.

Using SonarQube as a tool to perform automated code reviews and ensuring adherence to coding standards.

Tailoring SonarQube's rule set to suit specific project needs or coding practices.

Interpreting SonarQube's visual dashboards and reports to track code quality improvements over time.

Topics:

What Nexus Repository is and how it helps in managing software artifacts in a centralized location.

Guide to installing and configuring Nexus Repository for storing build artifacts, libraries, and dependencies.

Understanding artifact repositories in Nexus and how to manage them effectively.

Exploring different formats of repositories supported by Nexus, including Maven, Docker, and NPM.

How to integrate Nexus Repository with Jenkins or other CI/CD tools to automate artifact deployment.

Managing versions of artifacts and handling metadata to ensure traceability and consistency.

Configuring Nexus to proxy external repositories for caching dependencies and improving build efficiency.

Implementing security measures and access control policies in Nexus to protect sensitive artifacts.

Best practices for monitoring Nexus Repository's health and ensuring its availability.

Leveraging Nexus for managing release candidates and ensuring reliable artifact deployment during releases.

Topics:

Understanding the importance of high availability in systems design.

Exploring the concept and need for container orchestration.

Overview of tools available for container orchestration including Kubernetes.

Introduction to Kubernetes and its role in container orchestration.

Understanding the architectural components of Kubernetes.

Topics:

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

Introduction to the fundamental objects in Kubernetes.

Understanding Pods, the smallest deployable units in Kubernetes.

Role and functioning of Replica Sets in managing pods.

How Deployments automate the updating and rollback of applications.

Topics:

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

Exploring ClusterIP for internal cluster communication.

Understanding how NodePort exposes services outside of the cluster.

Using Load Balancers to distribute traffic evenly across services.

Configuring Ingress for external access to services within the cluster.

Topics:

Managing application configuration using Config Maps.

Securely storing sensitive information with Secrets.

Understanding the storage capabilities in Kubernetes with PV and PVC.

Exploring dynamic volume provisioning through Storage Classes.

Managing stateful applications with StatefulSets.

Topics:

Considerations for running Kubernetes in production environments.

Introduction to Azure Kubernetes Service (AKS).

Steps for setting up a Kubernetes cluster on AKS.

Practical guide to deploying applications on AKS.

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:

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