[Important] Compilation of Most Asked System Design, ML System Design Case Studies and LLM System Design

For your tech interviews ...With examples and code..

LLM System Design Case Studies

Understanding Transformers & Large Language Models: How They Actually Work - Part 1

Understanding Transformers & Large Language Models: How They Actually Work - Part 2

[Launching LLM System Design ] Large Language Models: From Tokens to Optimization: How They Actually Work - Part 1

[Launching LLM System Design #2] Large Language Models: From Architecture, Attention, and Fine-Tuning: How They Actually Work - Part 2

[LLM System Design #3] Large Language Models: Pre-Training LLMs: How They Actually Work - Part 3

[Important LLM System Design #4] Heart of Large Language Models: Encoder and Decoder: How They Actually Work - Part 4

[Important LLM System Design #12] Brain of LLM: Encoder-Decoder Model: How They Actually Work - Part 12

ML System Design Case Studies

[ML System Design Tech Case Study Pulse #15] Billions of Images : How Meta Animates AI-generated Images at Scale

[ML System Design Tech Case Study Pulse #14]Massive Billions of Personalized Recommendations in Real-Time: How Instagram Scaling Actually Works

[Most Asked : ML System Design Case Studies #26] Millions of Flights: How Expedia Forecasts Flight Prices at Scale in Real Time: How it Actually Works

[Most Asked : ML System Design Case Studies #27] Millions of Posts : How Linkedin Personalize Your Homepage Feed at Scale : How it Actually Works

[Most Asked : ML System Design Case Studies #25] Thousands of Rides - What Happens When You Open the Uber App at an Airport During Busy Hours: How it Actually Works

[Launching-ML System Design Tech Case Study Pulse #2] Million Of House Prices in Predicted Accurately in Real Time : How Zillow Actually Works

[ML System Design Tech Case Study Pulse #4 : Top Question] Predict Real-time Store Status to Billions of Users Worldwide: How Google Maps Actually Work

[ML System Design Tech Case Study Pulse #3 : Top Question] Recommending Million Of Items to Millions of Customer in Real Time: How Amazon Recommendation Actually Works

[Most Asked : System Design Tech Case Study Pulse #103] Massive 100+ Million Requests per Second : How Amazon Shopping Cart Actually Works

[Launching-ML System Design Tech Case Study Pulse #1]Handling Billions of Transaction Daily : How Amazon Efficiently Prevents Fraudulent Transactions (How it Actually Works)

[System Design Case Study #27] 3 Billion Daily Users : How Youtube Actually Scales

[System Design Tech Case Study Pulse #26] Processing 2 Billion Daily Queries : How Facebook Graph Search Actually Works

[System Design Tech Case Study Pulse #26] 1.5 Billion Swipes per Day : How Tinder Matching Actually Works

[System Design Tech Case Study Pulse #25] 500+ Million Users Daily : How Instagram Stories Actually Work

[System Design Tech Case Study Pulse #24] 2.9 Billion Daily Active Users : How Facebook News Feed Algorithm Actually Works

[System Design Tech Case Study Pulse #22] 20 Billion Messages Daily: How Facebook Messenger Actually Works

[System Design Tech Case Study Pulse #21] 8+ Billion Daily Views: How Facebook's Live Video Ranking Algorithm Works

[System Design Tech Case Study Pulse #17] How Discord's Real-Time Chat Scales to 200+ Million Users

[System Design Tech Case Study Pulse #15] 80 Million Photos Daily : How Instagram Achieves Real Time Photo Sharing

[System Design Tech Case Study Pulse #20] Serving 1 Trillion Edges in Social Graph with 1ms Read Times : How Facebook TAO works

[System Design Tech Case Study Pulse #2] How Lyft Handles 2x Traffic Spikes during Peak Hours with Auto scaling Infrastructure..

121 implemented projects that you can use for your portfolio listed below —

LLM, Machine Learning, Deep Learning, Data Science Projects Collection

Implemented Deep Learning Projects

• Implement Recurrent Neural Network using Keras to forecast Google Stock Closing Prices

• Implement RNN from scratch and Build Movie Review Sentiment Analysis System

• Build Complete Data Processing Pipeline (End to End project)

• Build a platform that uses Machine learning to Detect Fraudulent Transactions

• Implement Convolutional Neural Networks from Scratch with a Project

• Predicting House Prices with Multilayer Perceptrons

• Build EmotionLens - Emotion Classification Using Word2Vec Embeddings

• Build AutoTagger - Fast Keyword Tagging of News Articles using GloVe Embeddings

• Building Movie Recommender Systems using the MovieLens Dataset

• Implement Generative Adversarial Networks with MNIST Dataset: Simple GAN and DCGAN

• Implement Word Embeddings with a Project Part 2

• Implement BERT with a Project Part 2

• Implement BERT with a Project Part 1

• Implement LeNet with a Project

• Implement DenseNet with a Project

• Implement Word Embeddings with a Project - Part 1

• Implement RNN Part 2 with a Project

• Implement RNN Part 1 with a Project

• Implement LSTM GRU with a Project

• Implement Optimizers Part 2 with a Project

• Implement Optimizers - Part 1 with a Project

• Implement Modern CNN Architecture Part 2 with a Project

• Implement ResNet with a Project

• Implement Modern CNN Architecture Part 1 with a Project

Implemented LLM Projects

• Implement and Understand LLM API and Build Chatbot with a Project

• Implement and Understand OpenAI Agents with a Project

• Implement Tokenization, Embeddings, Building a Simple Language Model

• Sentiment Analysis on Custom Transformer Vs PreTrained Transformer

• Understand and Implement Sampling from an LLM (Project Based)

• Understand and Implement Fine Tuning(Project Based) - Part 2

• Understand and Implement Fine Tuning(Project Based) - Part 1

• Implement Different Transformer Architectures & Seq2Seq Model(Project Based)

• Understand and Implement Attention and Transformer Architecture(Project Based)

Implemented Machine Learning Projects

• Build Mall Customers Clustering App (Streamlit)

• Implement Tree Based Model & Hyperparameter tuning using Housing Dataset

• Complete Time Series Analysis & Forecasting using Air Passengers Dataset

• Complete Implementation of Support Vector Machines using Breast Cancer Dataset

• Complete End to End Machine Learning Project for Student Performance

• Complete Implementation of Spectral Clustering using Credit Card Dataset

• Implement Polynomial, Regression Ridge, Lasso, Baseline Model, Cross Validation

• Implement Univariate Analysis using Titanic Dataset

• Implement Multivariate Analysis Using Wine Quality Dataset

• Implement Multilayer Perceptrons (MLPs) from scratch

• Implement Multidimensional Scaling using Wine Quality Dataset

• Implement Linear Regression to predict house prices using California Housing Dataset

• Implement Linear Neural Networks, Generalization, Regularization and Weight Decay

• Implement Softmax Regression & Classification with Linear Neural Networks

• Complete Implementation of K-Nearest Neighbors (KNN) using Breast Cancer Dataset

• Implement Gradient Boosting with the UCI Heart Disease Dataset

• Implement Feature Engineering, Feature Imputation, Feature Transformation & Selection

• Complete Implementation of Density Clustering using Wholesale Customers Dataset

• Implement Decision Tree & Random Forests Classifiers using Drug prescriptions Dataset

• Implement Convolutional Neural Networks from scratch using CIFAR-10 Dataset

• Implement Bayesian Linear Regression and Model Evaluation

• Clustering Analysis (K-means) using Mall Customers Dataset

• Classification of Bank Loan Approvals Using Naive Bayes, Probability Distribution, MLE

• Image Compression & Feature Reduction with PCA

• Implement Ensemble Learning using Bootstrapping (Bagging Method) with a Project

• Implement Learning Curve Analysis Across Multiple ML Algorithms with a Project

• Implement Bias-Variance Tradeoff Analysis with Ensemble Methods

Implemented Data Analysis & Visualization Projects

• Customer Segmentation & Product Analysis Using KDE and Statistical Tests

• Complete Data Visualization and Analysis Using Penguins Dataset

• Wikipedia Topic Text Analysis with Advanced Data Collection

• Scraping and Analyzing Quotes with Pandas and BeautifulSoup

• Analyzing Restaurant Tips with NumPy and Pandas

• Data Visualization using Seaborn and Matplotlib

Implemented Real-World ML Applications

• Stroke Prediction Analysis

• Spotify Curator using ML

• Rotten Tomatoes Score Predictor Using Sentiment Analysis

• Real Time Customer Churn Prediction for Subscription Based Streaming Services

• Build Online Payment Fraud Detection Using Machine Learning

• Motor Vehicle Collision Severity Prediction in NYC

• Build League of Legends Esports Prediction Model

• Analysis of Electric Vehicle Charging Patterns

• Analyze and Model Crime Scope in NJ

• Communicable Disease Analysis Across California Counties

• Calorie Burn Prediction Model

• Customer Churn Prediction for Telco Company

• Movie Recommendation System (Evaluation Metrics Project)

• Iris Flower Classification (Evaluation Metrics Project)

Implemented Data Cleaning & Preprocessing Projects

• Advanced Data Cleaning in a Multi-country Economic Dataset

• Imputation Techniques in Medical Data (Diabetes)

• Handling Dirty Data in a Job Posting Dataset

• Exploratory Data Analysis in a Health Dataset (Heart Disease)

• Housing Price Prediction Model

• Handling Multi-source Data (Merging and Joining Datasets)

• Financial Data Cleaning and Predictive Modeling

• Weather Data Drift analysis and Correction Project

• Social Media Sentiment Analysis and Text Processing System

• Handling Imbalanced Datasets (Credit Card Fraud)

• Stock Price Analysis and Smoothing Project

• Dealing with Outliers in a Sales Dataset

• Customer Satisfaction Survey Analysis System

• Data Preprocessing in an E-Commerce Transaction Dataset

• Data Preprocessing in Retail (Customer) Dataset

Implemented Database & ETL Projects

• How to perform ETL (Extract, Transform, Load)

• How to perform Big Data Processing

• How to work with PostgreSQL

• How to perform Data Transformation and Preprocessing

• How to work with MySQL

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Let’s get started with new system design case studies-

Design TrueCaller

• What is True Caller

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low level design

• API Design

• Complete Detailed Design

Design Stock exchange Design System

• What is Stock Exchange System

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design Distributed Cache

• What is Distributed Cache

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Complete Code Implementation

Design Twilio

• What is Twilio

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Google Docs

• What is Google Docs

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design Doordash

• What is Doordash

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design Cache Mechanism

• What is Cache Mechanism

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Complete Code Implementation

Design MS Docs 

• What is MS Docs

• Important Features

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Complete Code Implementation

Design Zomato

• What is Zomato

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Complete Code Implementation

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Read more —

WHY you should take ONLY these courses to sky rocket your Data Science and ML Journey

Read about System Design 101 : Distributed Message Queue

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Projects Videos — 

All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).

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Learn how to efficiently use Python Built-in Data Structures

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Design Facebook Newsfeed

• What is Facebook News feed

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design Instagram

• What is Instagram

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Tinder

• What is Tinder

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Google Drive

• What is Google Drive?

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Messenger App

• What is Messenger App

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• Basic Low Level Design

• API Design

• High Level Design

• Complete Detailed Design

Design Linkedin

• What is Linkedin?

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Complete Code

Design Whatspp

• What is Whatsapp

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Dropbox

• What is Dropbox

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design Yelp

• What is Yelp

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Amazon Prime Video

• What is Amazon Prime Video

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design Web Crawler

• What is Web Crawler

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Code Implementation

Design API Rate Limiter

• What is Rate Limiter

• Important Features

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

Design URL shortener

• What is URL shortener

• Important Features

• Scaling Requirements

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

• Code implementation

Design Bookmyshow

• What is BookMyShow?

• Important Features

• Scaling Requirements — Capacity Estimation

• Data Model — ER requirements

• High Level Design

• Basic Low Level Design

• API Design

• Complete Detailed Design

More system design case studies coming soon! Follow - Link

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Things you must know in System Design -

• System design basics : https://bit.ly/3SuUR0Y

• Horizontal and vertical scaling : https://bit.ly/3slq5xh

• Load balancing and Message queues: https://bit.ly/3sp0FP4

• High level design and low level design, Consistent Hashing, Monolithic and Microservices architecture : https://bit.ly/3DnEfEm

• Caching, Indexing, Proxies : https://bit.ly/3SvyVDc

• Networking, How Browsers work, Content Network Delivery ( CDN) : https://bit.ly/3TOHQRb

• Database Sharding, CAP Theorem, Database schema Design : https://bit.ly/3CZtfLN

• Concurrency, API, Components + OOP + Abstraction : https://bit.ly/3sqQrhj

• Estimation and Planning, Performance : https://bit.ly/3z9dSPN

• Map Reduce, Patterns and Microservices : https://bit.ly/3zcsfmv

• SQL vs NoSQL and Cloud : https://bit.ly/3z8Aa49

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Data Science using Python

Pandas

Numpy

Advanced Pandas Techniques

Data Pre-processing

Handling missing values

Data Cleaning

Mean/mode/median Imputation

Hot Deck Imputation

Rescale Data

Binarize Data

Regression Imputation

Stochastic regression imputation

Feature Scaling

Data Augmentation

Read and Process Large Datasets

Data Visualization basics

Data Visualization Projects

Data Visualization using Plotly and Bokeh

Data Profiling

Summary Functions

Indexing

Grouping

Linear Regression

Multi Linear Regression

Polynomial Regression

Regression

Support Vector Regression

Decision Tree Regression

Random Forest Regression

Feature Engineering

GroupBy Features

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Spearman’s ρ

Pearson’s r

Kendall’s τ

Cramér’s V (φc)

Phik (φk)

Data Visualization

Data Visualization basics

Data Visualization Projects

Data Visualization using Plotly and Bokeh

Statistics

Random Variables

Statistical Inferences

Probability

Standard deviation and variance

Statistical Distributions

Hypothesis Testing

Normal distribution

t-distribution

Bernoulli distribution

confidence intervals

Data Collection and Data Cleaning

Data Collection

Data Cleaning

Data Manipulation

Join

Melt

Cut

Transform

Clean

Slicing

Reshaping

Filter

Group by

Pivot and Merge

Concatenate

MultiIndexing

Stacking

Hierarchical indexing

Aggregate

Summarize data

Linear Algebra for Machine Learning

Linear algebra concepts in Python

Matrix operations

Advanced linear algebra procedures

Supervised Learning

Regression

Supervised learning with probabilistic models

linear regression

Ordinary Least Squares

Linear Models

Linear and Quadratic Discriminant Analysis

Support Vector Machines

Stochastic Gradient Descent

Nearest Neighbors

Gaussian Processes

Cross decomposition

Naive Bayes

Decision Trees

Ensemble methods

Feature selection

Ridge Regression

Bias-variance tradeoff

Regression analysis

Bayesian Methods

Lagrange multipliers tool

sparse regression model

estimate covariants

Bayesian linear regression

Classification Algorithms

Classification using nearest neighbors

K-nearest neighbors

Bayes classifier

Supervised learning classification

perceptron algorithm

Logistic Regression

Kernel Methods

Gaussian Processes

kernel

kernelized perceptron

Support Vector Machines and Decision Trees

Hyperplanes with maximum margin method

SVM

decision tree-based classifiers

Grid search hyperparameters

Boosting and K-Means Clustering

Bagging and boosting techniques

Characteristics of K-means tools

Label encoder

Unsupervised Learning

Clustering Methods
K-means,

soft K-means

Gaussian mixture model

Principal Component Analysis and Markov Models

PCA basics

Implement PCA

Implement Markov chains using quantecon

Hidden Markov Models and Kalman Filtering

Hidden Markov Model

Markov models

Gaussian models

Forward/backward algorithm

Modeling

Model Training and Evaluation

Model Baselines

Model Tuning and Optimization

Model Review and governance

Automated Model retraining

Model Deployment and monitoring

Model Inference and Serving

Model Resource Management Techniques

Model Analysis

High-Performance Modeling

Model selection and evaluation

Cross-validation

Hyper-parameters Tuning

Performance Metrics

Validation curves

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Complete System Design with Implemented Case Studies and Code

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Description - This repository contains everything you need to become proficient in System Design .

60 days of Data Science and ML with project Series

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Description - This repository contains everything you need to become proficient in Data Science and Machine Learning .

Complete Data Structures and Algorithms and System Design Series

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Description - This repository contains everything you need to become proficient in Data Structures and Algorithms

Complete Data Engineering with Projects Series

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Description - This repository contains everything you need to become proficient in Data Engineering

Tech-Interview : Important Topics and Techniques

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Description - This repository contains everything you need to become tech interview Ready with most important tips and techniques.

ML/AI Research Papers Solved

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Description - This repository contains everything you need to become proficient in ML/AI Research and Research Papers .

Complete ML Ops With Projects Series

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Description - This repository contains everything you need to become proficient in MLOps

Complete Advanced SQL Series

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Description - This repository contains everything you need to become proficient in Advanced SQL

Time Series Analysis and Forecasting

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Description - This repository contains everything you need to become proficient in Time Series Analysis and Forecasting

Complete Pytorch with Projects Series

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Description - This repository contains everything you need to become proficient in PyTorch

Complete Scikit learn with projects

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Description - This repository contains everything you need to become proficient in Scikit learn

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