Platform Stack with AI

What is AI?

AI is the ability of machines to perform tasks that require human intelligence, like learning, reasoning, and problem-solving.

How AI works

AI uses data, algorithms, and computing power to make predictions and automate tasks.

AI in everyday life

Virtual assistants (Open AI, Deepseek), recommendation systems (Netflix, YouTube), and chatbots.

Application Overview:

Understanding the significance and types of applications.

Web Application Fundamentals:

Key components and basic concepts of web applications.

Web Technologies:

Essential technologies and frameworks used in web application development.

Software Development Life Cycle (SDLC):

Phases and methodologies for effective software development.

Agile & Scrum:

Principles, frameworks, and best practices for managing projects iteratively.

What is data?

Information collected from different sources (text, images, videos, numbers).

Types of Data:

Structured Data:

Organized in tables (databases, spreadsheets).

Unstructured Data:

Freeform content (videos, images, text, emails).

Semi-structured Data:

A mix of both (JSON, XML).

Data Storage:

Databases (SQL, NoSQL), cloud storage (AWS, Google Drive).

Data Analysis :

Finding useful information in data to help make better decisions.

Data Engineering:

Building systems and processes to collect, store, and process large datasets using tools like Hadoop and Spark.

Processing Power for AI:

AI needs strong computing resources to process large amounts of data.

Key Technologies:

CPU (Central Processing Unit):

General-purpose processor, used for basic AI tasks.

GPU (Graphics Processing Unit):

Specialized for AI training and deep learning.

TPU (Tensor Processing Unit):

Optimized for AI workloads (used by Google).

Edge Computing:

Running AI on devices instead of the cloud (e.g., AI in mobile phones).

Cloud AI Platforms:

Google Cloud AI, AWS AI, Microsoft Azure AI.

1. Software Development

AI aids code generation, bug fixing, and testing.

Example: GitHub Copilot.

2. Autonomous Systems

AI in self-driving cars, robotics, and drones.

Example: Tesla autopilot.

3. Healthcare

AI diagnoses discovers drugs, and aids research.

Example: AI for MRI and CT scan analysis.

4. Finance

AI detects fraud, predicts stock trends, and automates trading.

Example: AI-powered credit scoring.

5. Education

AI personalizes learning and automates grading.

Example: AI tutors like Duolingo.

6. Retail & E-Commerce

AI recommends products and manages inventory, and pricing.

Example: Amazon’s recommendation system.

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:

1. Introduction to Containerisation

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

2. Monolithic vs Microservices Architecture

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

3. Introduction to Virtualisation and Containerisation

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

4. Docker Architecture

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

5. Setting up Docker

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

6. Docker Registry, Images, and Containers

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

7. Running Docker Containers

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

8. Docker Volumes and Networks

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

9. Docker Logs and Tags

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

10. Dockerize Applications and Docker Compose

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

Topics:

1. Introduction to CI/CD & GitHub Actions

Overview of Continuous Integration (CI), Continuous Delivery/Deployment (CD), and the role GitHub Actions plays in automating these processes.

2. Benefits and Requirements of CI/CD with GitHub Actions

The advantages of adopting CI/CD practices using GitHub Actions, including tight integration with GitHub, free usage for public repositories, and flexibility with YAML-based workflows.

3. Setting Up GitHub Actions Workflows

Step-by-step guide to creating and configuring workflows in the .github/workflows directory.

4. Understanding GitHub Actions Syntax and Structure

Explanation of key components like name, on, jobs, runs-on, steps, uses, and run in workflow YAML files.

5. Events and Triggers

Using various events to trigger workflows (e.g., push, pull_request, schedule, workflow_dispatch).

6. Jobs and Steps Configuration

Defining jobs and steps within workflows to automate tasks like building, testing, and deploying code.

7. Actions Marketplace

Exploring and utilizing pre-built actions from the GitHub Actions Marketplace to simplify CI/CD tasks.

8. Creating Custom Actions

Developing custom actions for specific project needs.

9. Continuous Deployment with GitHub Actions

Implementing Continuous Deployment pipelines with GitHub Actions to automate software delivery to various environments (e.g., staging, production).

10. Secrets Management

Storing and using secrets securely in workflows to protect sensitive information like API keys and credentials.

11. GitHub Actions Integrations

Integrating GitHub Actions with other tools and platforms (e.g., Docker, AWS, Azure, Google Cloud, Slack) for a complete CI/CD solution.

Topics:

1. Introduction to SonarQube

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

2. Setting up SonarQube

Guide to installing and configuring SonarQube for code quality analysis.

3. Integrating SonarQube with CI/CD Pipelines

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

4. SonarQube Metrics and Rules

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

5. Code Coverage and Test Reporting

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

6. Detecting Bugs and Vulnerabilities

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

7. Refactoring with SonarQube Insights

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

8. SonarQube for Code Reviews

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

9. Customizing SonarQube Rules

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

10. SonarQube Dashboards and Reports

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

Topics:

1. Introduction to Nexus Repository

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

2. Setting up Nexus Repository

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

3. Managing Artifacts in Nexus

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

4. Nexus Repository Formats

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

5. Integrating Nexus with CI/CD Pipelines

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

6. Artifact Versioning and Metadata

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

7. Nexus Proxying External Repositories

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

8. Security and Access Control in Nexus

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

9. Nexus Repository Health and Monitoring

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

10. Nexus for Release Management

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

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. Cloud Concepts

Understanding the benefits and considerations of using cloud services.

Exploring Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS).

Differentiating between Public Cloud, Private Cloud, and Hybrid Cloud models.

Topics:

1. Azure Compute

Introduction to the types of compute services offered by Azure and their use cases.

2. Azure Storage

Overview of Azure's storage options and recommendations for different data types and usage scenarios.

3. Azure Networking

Basic concepts of Azure networking solutions including virtual networks, subnets, and connectivity options.

4. Azure Database Services

Introduction to Azure's database services for relational and non-relational data.

Topics:

1. Azure Pricing and Support

Understanding Azure pricing, cost management tools, and Azure support plans and services.

2. Azure Governance

Azure governance methodologies, including Role-Based Access Control (RBAC), resource locks, and Azure Policy.

Topics:

1. Azure Portal and Azure CLI

Utilizing the Azure Portal and Azure Command-Line Interface (CLI) for managing Azure services.

2. Azure Management Tools

Introduction to Azure management tools like Azure Monitor, Azure Resource Manager, and Azure Policy for efficient resource management.

Topics:

1. App Services

Overview of Azure App Service plans, networking for an App Service, and container images.

Understanding how to deploy and manage web apps and APIs using Azure App Services.

Introduction to Data Engineering:

Roles, Responsibilities & ETL vs. Data Engineering

Cloud Basics & Azure Overview:

IaaS, PaaS, SaaS & Key Azure Storage Components

Azure SQL Database & Data Integration Basics:

SQL Server Deployment, Firewall Rules, and Azure Data Factory (ADF)

Synapse SQL Pools: MPP, Data Movement, and Performance Optimization

Azure Data Factory (ADF): ETL Pipelines, Data Flow, Incremental Loading & Monitoring

Advanced Integration: On-Prem Data, CDC, and Real-Time Data Capture

Azure Storage & Data Lake:

ADLS Gen2, Security (RBAC, SAS, ACLs), and Best Practices

Real-Time Data Processing:

Azure Stream Analytics, Event Hubs, IoT Data Streaming

Performance & Monitoring:

Tuning, Disaster Recovery, and Cost Optimization

Databricks & Spark Essentials:

Cluster Configuration, PySpark, DataFrames, and SQL

Data Pipelines & Machine Learning:

Delta Lake, MLlib, Data Exploration & Visualization

Security & Real-Time Analytics:

AD Integration, Streaming Data with Event Hubs

CI/CD & Automation:

Azure Logic Apps, Version Control, and Best Deployment Practices

Security & Compliance:

Managing Access, Data Governance, and Operational Excellence

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.

Why Python? (Simplicity, Libraries, Community Support).

Setting up Python (Anaconda, Jupyter Notebook, VS Code.

Data Types (int, float, string, list, tuple, dict).

Control Structures (if-else, loops).

Functions & Modules.

File Handling.

Classes & Objects

Inheritance & Polymorphism.

Encapsulation & Abstraction.

How OOP is used in AI (e.g., Model Classes in TensorFlow/PyTorch).

NumPy – Arrays & Numerical Computation.

Pandas – Data Manipulation & Analysis.

Matplotlib & Seaborn – Data Visualization.

Reading/Writing CSV, Excel, JSON.

Handling Missing Data.

Data Cleaning & Transformation.

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 Best Practices and Principles

1. SRE Roles and Responsibilities

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

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.

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. Infra Metrics and Application Metrics

Scraping the infrastructure metrics using Node exporter and application metrics using Blackbox exporter

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.

What is Generative AI?

Key Applications

Text (Chatbots, Content Generation)

Image (DALL·E, MidJourney)

  Audio (Music Generation, Voice Synthesis)

  Code (Cursor, Copilot)

Evolution of GenAI:

From Rule-Based Systems to Deep Learning

Comparison of Generative Models (GANs, VAEs, LLMs)

Challenges in GenAI (Bias, Hallucinations, Ethical Considerations)

What is Prompt Engineering?

Importance of Effective Prompt Design

Basic Prompting Techniques:

Instruction-Based Prompts

Few-Shot & Zero-Shot Learning

Advanced Prompt Engineering:

 Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Structured vs. Unstructured Prompts

Experimenting with LLMs (Using GPT-4, Claude, or LLaMA)

  Transformers & LLMs

  Why Transformers? (Limitations of RNNs & LSTMs)

Key Components:

Self-Attention Mechanism

Multi-Head Attention

Encoder-Decoder Architecture

Evolution of Transformers:

From BERT to GPT, T5, and Beyond

Large Language Models (LLMs)

What are LLMs?

Pre Training vs. Fine-Tuning

Popular LLM Architectures:

GPT (OpenAI GPT-4,O3)

DeepSeek

BERT (Contextual Embeddings)

T5 (Text-to-Text Models)

Challenges in LLMs:

Bias & Ethical Issues

Scalability & Cost

Model Hallucinations

GANs & VAEs (Other Generative Models)

Generative Adversarial Networks (GANs):

What are GANs?

How GANs Work: Generator & Discriminator

Applications of GANs (DeepFake, Image Generation,Super-Resolution)

Autoencoders & Variational Autoencoders (VAEs):

What are Autoencoders?

Difference Between Autoencoders & VAEs

 Applications (Data Denoising, Anomaly Detection) Lightweight Models (LIMs)

What are Lightweight AI Models?

Difference Between LIMs & LLMs

Use Cases of LIMs in Edge AI

LangChain

  What is LangChain

  Building Modular LLM Workflows

Practical Applications

Hugging Face

Overview of Hugging Face Transformers & Datasets

How to Fine-Tune & Deploy Models

Vector Databases & Retrieval-Augmented Generation (RAG)

  Introduction to Vector Databases (Pinecone, Weaviate, FAISS)

  Understanding RAG and Its Role in GenAI

What are AI Agents?

Difference Between AI Agents and Traditional AI Systems

Key Characteristics:

Autonomy

  Goal-Oriented Behavior

Tool Usage & Execution

Real-World Applications of AI Agents

  CrewAI (Multi-Agent Collaboration)

Overview: How CrewAI enables multi-agent workflows

 Components: Roles, Tasks, Tools, Memory

Use Case: Automating research and content generation

N8N (Workflow Automation for AI Agents)

  What is N8N?

  Connecting AI Agents with APIs and automation

Use Case: AI-driven task execution with n8n

  Langflow (Visual Agent Orchestration)

Introduction to Langflow

Building AI Agent workflows with a drag-and-drop interface

Use Case: Rapid prototyping and deployment of AI Agents

  Creating AI Agents using CrewAI + Lang Flow

Automating tasks with CrewAI + N8N

Multi-agent collaboration for business workflows

APIs: FastAPI.

Containerization: Docker.

Cloud Platforms: AWS, Google Cloud, Azure.

Project: Deploy a sentiment analysis model as a web app.

Version Control:  MLflow.

  CI/CD Pipelines: GitHub Actions

 Monitoring: Prometheus, Grafana.