Platform Engineering With AI

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

Introduction to Linux OS

Exploring the fundamentals of the Linux operating system.

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.

File Management and vi Editor

Managing files and editing them using the vi editor.

Archives and Package Management

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

System Installation and Package Managers

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

Users, Groups, and Permissions

Managing users and groups, and configuring permissions.

Networking Basics: IP Address, Protocols, & Ports

Introduction to networking in Linux: IP addresses, protocols, and ports.

Firewalls and Security Measures

Configuring firewalls and understanding basic security measures.

Load Balancers

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

Introduction to Version Control System

Basics of version control and its importance in software development.

Centralised vs Distributed Version Control System

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

Git & GitHub Introduction

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

Git Workflow

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

GitHub for Collaboration

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

Git Branching Model

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

Git Merging and Pull Requests

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

Git Rebase

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

Handling Detached Head and Undoing Changes

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

Advanced Git Features: Git Ignore, Tagging

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

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.

Introduction to CI/CD & Github Actions

Basics of Continuous Integration, Continuous Deployment, and how GitHub Actions facilitates

CI/CD within the GitHub repository ecosystem.

Benefits and Requirements of CI/CD

Advantages of CI/CD and what's needed to implement it successfully, emphasizing GitHub's role in version control and collaboration.

Setting Up GitHub Actions

Creating and configuring workflows using YAML files in a GitHub repository.

Build Tools and Repository Management

Overview of build tools (e.g., Make, Gradle, npm) and how GitHub Actions integrates with GitHub repositories for CI.

Workflow Configuration

Defining jobs and steps within a GitHub Actions workflow to automate build, test, and deployment tasks.

Automating with Workflow Triggers

Using events like code pushes, pull requests, or schedules to trigger CI/CD workflows automatically.

GitHub Actions Workflows Creating workflows that orchestrate CI/CD pipelines, including building, testing, and deploying code.

Utilizing Actions and Reusable Workflows

Leveraging pre-built actions from the GitHub Marketplace and creating custom actions for specific tasks.

Creating reusable workflows to modularize CI/CD processes.

Continuous Deployment with GitHub Actions

Strategies and examples of deploying applications to various environments (e.g., staging, production) using GitHub Action

Introduction to High Availability

Understanding the importance of high availability in systems design.

Introduction to Container Orchestration

Exploring the concept and need for container orchestration.

Container Orchestration Tools

Overview of tools available for container orchestration, including Kubernetes.

Overview of Kubernetes

Introduction to Kubernetes and its role in container orchestration.

Kubernetes Architecture

Understanding the architectural components of Kubernetes.

Components of Kubernetes

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

Kubernetes Objects

Introduction to the fundamental objects in Kubernetes.

What is Azure DevOps?

An overview of Azure DevOps services and its ecosystem.

Azure Boards

Introduction to project management using Azure Boards.

Azure Repos

Managing code repositories with Azure Repos.

Azure Pipelines

Automating builds, tests, and deployments with Azure Pipelines.

Creating Pipelines in Azure DevOps

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

Agile Project Management Best Practices

Implementing agile methodologies using Azure Boards.

Basic Concepts of Azure Boards

Understanding work items, sprints, and scrum features.

Connecting Boards to GitHub

Integrating Azure Boards with GitHub repositories.

Work Items and Sprints

Managing tasks and sprints in Azure Boards for agile development.

Azure Boards Integrations

Enhancing Azure Boards with integrations for extended functionalities.

Introduction to Azure Repos

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

Branches and Cloning in Azure Repos

Managing branches and cloning repositories for development workflows.

Import Code from GitHub

Steps to import existing codebases from GitHub into Azure Repos.

Search Your Code in Repos

Utilising search functionalities within Azure Repos for code management.

Azure Repos Integrations

Extending Azure Repos capabilities with external integrations.

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.

Azure Compute

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

Azure Storage

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

Azure Networking

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

Azure Database Services

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

Azure Pricing and Support

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

Azure Governance

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

Azure Portal and Azure CLI

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

Azure Management Tools

Introduction to Azure management tools like Azure Monitor, Azure Resource Manager, and Azure

Policy for efficient resource management.

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

Types of Machine Learning:
Supervised (Classification, Regression)

Unsupervised (Clustering, Dimensionality Reduction)

Reinforcement Learning (Brief Overview)

Common Algorithm Families & Use Cases:
Linear Models, Tree-Based Models (Random Forest, Gradient Boosting)

Neural Networks basics. Focus on input/output expectations.

The Model Training Loop:
Features & Labels, Data Splitting (Training, Validation, Test sets - importance of hold-out sets)

Loss Functions, Optimization Algorithms (e.g., Gradient Descent)

Model Evaluation:
Key metrics for Classification (Accuracy, Precision, Recall, F1-Score, AUC)

Regression (RMSE, MAE, R-squared)

Why standardized evaluation is critical for automated MLOps pipelines.

Data as Foundation: Understanding the impact of data quality, volume, and relevance on model performance.

Data Acquisition & Storage:
Overview of sources (Databases, Data Lakes, APIs) and formats.

Feature Engineering:
Creating meaningful inputs for models.

How feature engineering choices impact deployment complexity.

Data Preprocessing & Scaling:
Techniques (Normalization, Standardization, Encoding).

Need for consistency between training and inference preprocessing.

Identifying & Preventing Data Leakage:
Understanding how information from the test set can inadvertently influence training.

Model Serialization:
Saving trained models (formats like Pickle, joblib, SavedModel, ONNX).

Pros and cons regarding compatibility and deployment.

Model Signatures & Schemas:
Defining expected input and output formats.

Environment Dependencies:
Capturing required libraries and versions (Python, OS packages).

Underscoring the need for tools like Conda and Docker.

Model Versioning:
The necessity of tracking different iterations of trained models.

Model Size & Inference Performance:
Understanding the trade-offs between model complexity, prediction speed, and resource consumption.

Introduction: Moving Beyond Ad-hoc ML.

Core MLOps Principles:
Automation, Reproducibility, Collaboration, Continuous Monitoring, Governance.

Overview of the Lifecycle Stages:
From Data to Production and Back. Key Differences from Traditional SDLC.

ML Development & Experimentation:
Business Understanding, Data Prep, Feature Engineering, Model Training/Tuning.

MLOps Integration:
Code Versioning (Git), Experiment Tracking Setup, Environment Definition.

Training Operationalization (Continuous Training - CT): Packaging Code/Env (Docker), Automated Training Pipelines.

Data/Model Validation in Pipelines, Experiment Tracking Integration.

Output: Registered Model Candidate.

Model Validation & QA:
Automated Testing (Performance, Robustness).

Responsible AI Checks (Fairness, Explainability), Staging Environments, Quality Gates.

Model Deployment (Continuous Deployment - CD): Packaging for Inference, Defining Deployment Infrastructure (IaC).

Automated Deployment Pipelines, Deployment Strategies (Blue/Green, Canary).

Prediction Serving:
Real-time APIs vs. Batch Inference execution.

Monitoring & Feedback Loop:
System Health Monitoring, Model Performance Monitoring.

Data/Concept Drift Detection, Logging, Alerting, Triggering Retraining.

Iteration & Governance:
Continuous Improvement, Model Lineage Tracking, Compliance.

The Challenge: Managing the Complexity of ML Development.

What is MLflow? An Open Platform for the ML Lifecycle.

MLflow's Core Philosophy: Open Source, Framework Agnostic, API-first.

Overview of MLflow Components (Tracking, Projects, Models, Registry).

Core Concepts: Experiments, Runs, Parameters, Metrics, Artifacts (files, plots, models), Tags.

Logging to MLflow: Using the Client API (Python, R, Java, REST) and CLI.

The Tracking Server: Storing run data (local files, SQLAlchemy compatible DB, remote server).

The MLflow UI: Visualizing, comparing, and searching runs and experiments.

Autologging capabilities for popular libraries.

Standardizing Code Execution: Packaging ML code for reproducibility.

The MLproject File: Defining entry points and environment dependencies (Conda, Docker).

Running Projects: mlflow run command (local execution, remote execution e.g., on Databricks).

Parameterizing runs.

Standard Model Format: Packaging models for downstream use.

Model Flavors: Framework-specific formats (e.g., python_function, scikit-learn, TensorFlow, PyTorch, ONNX).

Saving and Loading Models with MLflow.

Built-in Deployment Tools (e.g., local REST server, Spark UDF creation).

Centralized Model Management: Storing, versioning, and governing models.

Key Concepts: Registered Models, Model Versions, Stages (Staging, Production, Archived), Annotations & Descriptions.

Workflow: Registering models, transitioning stages, fetching specific model versions/stages.

What is Kubeflow? The ML Toolkit for Kubernetes.

Why Kubeflow? Solving Portability, Scalability, Composability on K8s.

Architecture: Running as applications/operators on Kubernetes.

Use Cases: End-to-end orchestration, hybrid/multi-cloud ML.

Kubeflow Pipelines: Building, deploying, managing ML workflow DAGs (Python SDK, UI).

KServe (KFServing): Standardized, scalable Model Serving on Kubernetes (Serverless inference, traffic splitting).

Training Operators: Simplifying distributed training (TFJob, PyTorchJob, etc.).

Katib: Automated Hyperparameter Tuning and NAS.

Notebooks: Managed JupyterLab environments integrated with the cluster.

Metadata (MLMD): Artifact and lineage tracking backend.

Other Components (Central Dashboard, Security features).

Installation Overview: Complexity and distribution options.

Interacting with Components (UI, SDKs, kubectl).

Example Workflow: Building a simple pipeline, deploying a model with KServe.

Positioning vs. Managed Services (e.g., Azure ML, SageMaker, Vertex AI): Control vs. Convenience trade-offs.

Challenges: Learning curve, operational overhead.

What is Databricks? Unified platform for Data Engineering, Data Science, and ML.

Key Concepts: Workspaces, Notebooks, Clusters, Databricks File System (DBFS), Delta Lake, Jobs.

Databricks Machine Learning Runtime: Optimized environment with pre-installed libraries.

MLflow as a Managed Service: Hosted Tracking Server & Model Registry per workspace.

Seamless Integration: No separate setup required.

Automatic Logging (Autologging): Simplified tracking for common ML frameworks within Databricks notebooks and jobs.

UI Integration: Accessing MLflow Experiments and Model Registry directly within the Databricks UI.

Tracking from Notebooks and Jobs: Automatic linking of runs to notebook revisions or job IDs.

Managed Model Registry Features: UI-driven workflows, webhooks, integration with Databricks Model Serving.

Databricks Model Serving: Deploying registered MLflow models as scalable REST endpoints (Serverless Real-Time Inference).

Integration with Delta Lake: Tracking data lineage, using Delta tables as data sources.

Integration with Databricks Feature Store: Tracking features used in model training.

Collaboration Features: Sharing experiments and models within the workspace.

Comparison: Managed Databricks MLflow vs. Open Source MLflow deployment.

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:

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