Complete Guide for ML System Design (with Case Studies)

December Goals...

Dec 26, 2023

Hi All,

Previously we covered 200+ system design case studies. In the new segment; we are moving forward with ML System Design Case Studies and would be covering 300+ ML System design case studies for your ML System Design interviews as detailed below -

Pre-requisite ( how to get started) -

MUST Complete these data science and ML courses ( since ML system design case studies will involve lots of concepts covered in these courses)

Start here -

Day 1 of ML System Design Case Studies Series : ML System Design Basics

Recommendation Systems

Companies: Netflix, Amazon
Approaches: Collaborative filtering, content-based filtering, hybrid methods
Real-World Examples: Netflix suggests movies based on viewing habits; Amazon analyzes user behavior for tailored product suggestions.

Image Recognition

Companies: Google, Facebook
Techniques: Leveraging CNNs (Convolutional Neural Networks)
Real-World Examples: Google Photos uses ML-based facial recognition for photo organization; Facebook implements accurate object/scene identification.

Natural Language Processing

Companies: Google, Apple
Models: BERT, GPT (Transformer models)
Real-World Examples: Google Assistant employs NLP techniques for language understanding; Siri responds to user queries using transformer-based models.

Fraud Detection

Industries: Banks, Financial Institutions
Approach: Analyzing transaction data for abnormal patterns
Real-World Examples: Banks utilize ML to flag potential fraudulent activities by analyzing transaction data.

Autonomous Vehicles

Companies: Tesla, Waymo
Technologies: Sensor data processing, computer vision
Real-World Examples: Tesla vehicles use ML for autonomous features; Waymo employs computer vision for self-driving capabilities.

Healthcare (Diagnosis and Prognosis)

Institutions: Various Hospitals, Medical Institutions
Application: Analyzing medical data for diagnostics, patient outcome predictions
Real-World Examples: ML assists in disease detection from medical scans, aiding in diagnosis and prognosis.

Online Advertising

Companies: Google, Facebook
Utilization: ML-based optimization for ad targeting, engagement
Real-World Examples: Google Ads targets users based on search history; Facebook optimizes ad targeting using ML algorithms.

E-commerce Product Recommendation

Companies: Amazon, Alibaba
Approach: Analyzing user behavior for tailored product suggestions
Real-World Examples: Amazon suggests products based on browsing history; Alibaba tailors recommendations using user data.

Healthcare Predictive Analytics

Organizations: Hospitals, Healthcare Institutions
Application: Forecasting patient admission rates for resource planning
Real-World Examples: Hospitals predict patient influx for staffing optimization using predictive analytics.

Energy Consumption Forecasting

Entities: Utility Companies, Energy Corporations
Strategy: Algorithms to optimize energy distribution
Real-World Examples: Utility companies manage energy efficiently using ML-based forecasting for consumption patterns.

Personalized Learning Platforms

Platforms: Khan Academy, Coursera
Approach: Adapting educational content based on student performance
Real-World Examples: Platforms like Khan Academy offer personalized learning experiences, adjusting content based on individual progress.

Insurance Risk Assessment

Entities: Insurance Companies, Underwriters
Strategy: Assessing risks using historical data for policy pricing
Real-World Examples: Insurers evaluate risks using ML models, leveraging historical data for pricing policies.

Smart Traffic Management

Stakeholders: City Governments, Traffic Control Centers
Utilization: Using algorithms and sensors for traffic optimization
Real-World Examples: Cities manage traffic flow using ML-based systems and sensor data for optimized routes.

Supply Chain Optimization

Industries: Retail Chains, Logistics Companies
Practice: Forecasting demand and managing inventory efficiently
Real-World Examples: Companies streamline supply chains, predicting demand and managing inventory efficiently through ML.

AI-Powered Virtual Personal Assistants

Providers: Amazon (Alexa), Apple (Siri)
Functionality: Understanding user preferences for daily tasks
Real-World Examples: Alexa and Google Assistant assist users based on learned preferences and interactions using ML.

Climate Change Prediction

Institutions: Climate Research Institutions, Environmental Agencies
Objective: Developing models to predict climate trends
Real-World Examples: Scientists forecast climate changes and trends using ML-driven models.

Human Resources Talent Acquisition

Sectors: HR Firms, Recruitment Agencies
Application: Matching candidate skills with job requirements
Real-World Examples: HR departments screen and match candidates using ML-driven tools.

Personalized Fashion Recommendations

Sectors: Fashion Retailers, E-commerce Platforms
Service: Suggesting fashion items based on user preferences
Real-World Examples: Fashion retailers offer personalized suggestions using ML-driven recommendations.

Smart Grid Optimization

Entities: Utility Companies, Grid Operators
Action: Optimizing power distribution using algorithms
Real-World Examples: Utilities manage grids efficiently, optimizing power distribution with ML.

Automated Medical Image Diagnosis

Facilities: Hospitals, Medical Imaging Centers
Capability: Identifying diseases from medical scans
Real-World Examples: ML aids in disease detection and diagnosis from medical imaging.

Problem: Detecting Duplicate Questions on Q&A Platforms

Problem Description: Identify similar or duplicate questions on platforms like Quora.
Real-World Scenario: Quora uses ML models to detect and merge duplicate questions.

Problem: Customer Review Analysis

Problem Description: Understanding themes or sentiments in customer reviews.
Real-World Scenario: Amazon uses ML to analyze reviews and improve products based on feedback.

Problem: Author Identification

Problem Description: Identifying an author from multiple candidates based on writing style.
Real-World Scenario: Forensic departments use ML to identify authors of anonymous texts.

Problem: Machine Translation for Low-Resource Languages

Problem Description: Translating languages with limited available data.
Real-World Scenario: Google Translate employs ML for translating low-resource languages.

Problem: Text-based Emotion Recognition

Problem Description: Detecting emotions expressed within text.
Real-World Scenario: Social media platforms use ML to understand user emotions in posts.

Problem: Abstractive Text Summarization

Problem Description: Generating abstract summaries without direct text extraction.
Real-World Scenario: News aggregators employ ML to create abstract article summaries.

Problem: Chatbot Development

Problem Description: Creating conversational AI using NLP techniques.
Real-World Scenario: Siri and Google Assistant use ML for conversational interactions.

Problem: Detecting Offensive Language in Text

Problem Description: Identifying and flagging offensive language in social media posts.
Real-World Scenario: Twitter uses ML to filter out offensive tweets.

Problem: Cross-lingual Information Retrieval

Problem Description: Retrieving relevant documents in one language based on queries in another language.
Real-World Scenario: Multilingual search engines employ ML for cross-lingual information retrieval.

Problem: Aspect-based Sentiment Analysis

Problem Description: Analyzing sentiment toward specific aspects within reviews.
Real-World Scenario: Yelp uses ML to determine sentiments about various aspects of restaurants.

Problem: Medical Text Classification

Problem Description: Categorizing medical texts into various domains like diseases, symptoms, treatments.
Real-World Scenario: IBM's Watson Health applies ML to categorize medical texts aiding in diagnoses.

Problem: Language Generation using Transformers (GPT, BERT)

Problem Description: Generating human-like text using transformer-based models.
Real-World Scenario: OpenAI's GPT-3 generates human-like text for various purposes like content creation.

Problem: Text-Based Entailment Recognition

Problem Description: Determining if one statement logically follows another.
Real-World Scenario: Google uses entailment recognition to improve search query understanding.

Problem: Stance Detection in Text

Problem Description: Identifying the perspective expressed in text towards a specific topic.
Real-World Scenario: Media outlets use stance detection to analyze public sentiment towards political topics.

Problem: Legal Document Classification

Problem Description: Classifying legal documents into categories or extracting key information.
Real-World Scenario: LexisNexis uses ML to categorize legal documents for efficient retrieval.

Problem: Text-based Recommendation System

Problem Description: Recommending articles, books, or movies based on user preferences.
Real-World Scenario: Netflix uses ML to suggest movies based on viewing history.

Problem: Text-based Gender Prediction

Problem Description: Predicting the gender of an author based on their writing style.
Real-World Scenario: Social media platforms use gender prediction for targeted advertising.

Problem: Machine Translation for Domain-Specific Jargon

Problem Description: Creating translation models specific to certain industries or domains.
Real-World Scenario: Medical institutions use ML for accurate translation of medical texts.

Problem: Text-Based Intent Classification for Chatbots

Problem Description: Classifying user intents in conversational data for chatbot systems.
Real-World Scenario: Chatbot platforms like Dialogflow use intent classification for user interaction.

Problem: Text-Based Personality Prediction

Problem Description: Predicting personality traits of individuals based on their written text.
Real-World Scenario: Social media platforms use personality prediction for targeted content delivery.

Problem: Text-based Time Series Forecasting

Problem Description: Forecast future text data trends based on historical patterns.
Real-World Scenario: Bloomberg uses ML to forecast market trends based on historical news articles, aiding financial predictions.

Problem: Text-based Event Detection

Problem Description: Detect and categorize events or incidents mentioned in news articles or social media posts.
Real-World Scenario: Google News employs ML to detect and highlight major events or incidents from news articles for better news categorization.

Problem: Text-based Geo-location Prediction

Problem Description: Predict the geographical location or origin of a text based on its content.
Real-World Scenario: Twitter uses ML to predict tweet locations based on user-generated content or context.

Problem: Text-based Product Review Generation

Problem Description: Generate product reviews based on descriptions or features.
Real-World Scenario: Amazon uses ML to generate product reviews automatically based on item descriptions for better customer insights.

Problem: Multi-lingual Sentiment Analysis

Problem Description: Perform sentiment analysis across multiple languages.
Real-World Scenario: Google Translate uses ML to analyze sentiments in various languages, aiding in accurate translations and understanding user emotions.

Problem: Text-based Fake Review Detection

Problem Description: Detect fake or manipulated reviews among genuine ones.
Real-World Scenario: Yelp uses ML to detect suspicious reviews by analyzing various review factors.

Problem: Text-based Image Captioning

Problem Description: Generate captions for images based on their content.
Real-World Scenario: Google Photos uses ML to automatically generate captions for uploaded images.

Problem: Text-based Argument Mining

Problem Description: Identify arguments, claims, and evidence within text.
Real-World Scenario: Research institutions use ML to mine and extract arguments from academic papers or debates.

Text-based Semantic Analysis:

Problem Description: Determine the semantic similarity between two pieces of text.
Real-World Scenario: Semantic Scholar uses ML to suggest related research papers based on semantic similarity analysis.

Text-based Customer Support Chat Analysis:

Problem Description: Analyze customer support chat logs to improve service quality.
Real-World Scenario: Zendesk uses ML to analyze chat logs, identifying common issues and improving response times.

Text-based Cognitive Bias Detection:

Problem Description: Detect cognitive biases in written text.
Real-World Scenario: Researchers use ML to study and detect biases in news articles or research papers.

Text-based Hate Speech Detection:

Problem Description: Identify hate speech or discriminatory language in texts.
Real-World Scenario: Facebook employs ML to detect and moderate hate speech on its platform.

Text-based Profanity Filter:

Problem Description: Develop a filter to detect and mask profane language in text.
Real-World Scenario: Reddit uses ML to filter and mask inappropriate language in comments.

Text-based Risk Assessment:

Problem Description: Assess risk factors based on text data in various domains like finance or health.
Real-World Scenario: J.P. Morgan uses ML to assess financial risks by analyzing textual data from various sources.

Text-based Job Description Analysis:

Problem Description: Analyze job descriptions to understand trends or requirements in specific industries.
Real-World Scenario: LinkedIn uses ML to analyze job descriptions for better job recommendations and industry insights.

Text-based Algorithmic Trading Signals:

Problem Description: Use news articles or financial reports to generate signals for algorithmic trading.
Real-World Scenario: Hedge funds use ML to generate trading signals based on textual analysis of financial news.

Text-based Citation Recommendation:

Problem Description: Recommend citations or related research papers based on a given article.
Real-World Scenario: Academic databases employ ML to suggest related articles for research papers.

Text-based Product Demand Forecasting:

Problem Description: Forecast demand for products based on textual data from sales or reviews.
Real-World Scenario: eBay uses ML to forecast demand for products based on text analysis of reviews and sales data.

Text-based Voice Assistant Enhancement:

Problem Description: Improve voice assistant responses by analyzing user queries or commands.
Real-World Scenario: Apple uses ML to improve Siri's responses by analyzing user queries.

Text-based Plagiarism Detection:

Problem Description: Detect instances of plagiarism in written texts.
Real-World Scenario: Turnitin employs ML to detect plagiarism in academic submissions.

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