Launching soon - 31 Frequently Asked ML System Design Case Studies ( End to End)
ML System Design Made Easy...
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) -
Start here -
Day 1 of ML System Design Case Studies Series : ML System Design Basics
Prevent Fraudulent Transactions
Amazon employs machine learning to prevent fraudulent transactions.
Challenge: Identifying and blocking suspicious activities to protect customers and sellers.
ML algorithms analyze transaction patterns, user behavior, and various data points.
Detects anomalous activities and flags potential fraud in real-time.
Example: Temporarily suspends an account making high-value purchases from multiple locations.
Recommend Complementary Items
Netflix uses machine learning to suggest complementary shows or movies.
Challenge: Engaging users by recommending content that aligns with their preferences.
ML algorithms analyze viewing history, user ratings, and similar profiles.
Suggests content that complements the user's interests.
Example: Recommends another comedy show after the user finishes watching a comedy series.
Forecast Demand for Airport Rides
Uber employs machine learning to forecast demand for rides at different airports.
Challenge: Optimizing driver allocation and ensuring prompt service during peak times.
ML models analyze historical ride data, flight schedules, weather forecasts, and events.
Predicts the demand for rides accurately.
Example: Allocates more drivers to the airport before a major holiday weekend.
Prevent Advertiser Churn
Google Ads uses machine learning to prevent advertisers from discontinuing ad campaigns.
Challenge: Identifying factors that might lead to churn and intervening to retain advertisers.
ML algorithms analyze ad performance metrics, user feedback, and industry trends.
Predicts advertisers at risk of churning.
Example: Prompts optimization strategies to prevent a drop in campaign performance.
Generate Ad Headlines
Facebook applies machine learning to generate engaging ad headlines.
Challenge: Creating compelling and relevant headlines that attract user attention.
NLP models analyze ad content, user preferences, and historical ad performance.
Generates impactful headlines for higher click-through rates.
Example: Generates ad headlines based on advertiser input and historical data.
Recommend Items to Order
Amazon leverages machine learning to recommend products for customers' next purchase.
Challenge: Personalizing suggestions based on user behavior and preferences.
ML algorithms analyze purchase history, browsing behavior, and user preferences.
Suggests relevant products after a purchase.
Example: Suggests complementary items like lenses or tripods after buying a camera.
Estimate House Market Value
Zillow uses machine learning to estimate house market values accurately.
Challenge: Providing reliable property valuations for homeowners and buyers.
ML models analyze property data and historical sale prices.
Predicts a property's market value.
Example: A homeowner uses Zillow to estimate their house's value based on features and recent sales.
Identify User Interests
Pinterest utilizes machine learning to identify user interests and preferences.
Challenge: Personalizing content recommendations to enhance user engagement.
ML algorithms analyze user interactions, saved pins, and browsing behavior.
Suggests relevant content based on individual preferences.
Example: Recommends home decor content to a user regularly saving related pins.
Optimize Menu Sorting Order
McDonald's employs machine learning to optimize the sorting order of menu items.
Challenge: Maximizing sales and enhancing user experience through item arrangement.
ML algorithms analyze historical sales data, user preferences, and contextual factors.
Determines the most appealing menu item arrangement.
Example: Rearranges menu items during breakfast hours to prioritize breakfast meals and coffee.
Diagnose Production Incidents
Google utilizes machine learning to diagnose production incidents using Large Language Models (LLMs).
Challenge: Analyzing vast amounts of system logs and identifying patterns.
LLMs process unstructured text data from logs, learning normal system behavior.
Identifies anomalies or error patterns indicating potential incidents.
Example: Identifies a rare error pattern in system logs, alerting the operations team for rectification.
Optimize Courier Waiting Time
UPS uses machine learning to optimize courier waiting time during package pickups and deliveries.
ML algorithms analyze traffic patterns, delivery schedules, and historical data.
Predicts optimal pickup times and routes for couriers.
Example: Optimizes courier routes based on real-time traffic conditions, reducing wait times.
Predict Delivery Times
Uber Eats employs machine learning to predict delivery times accurately.
ML models analyze historical delivery data, traffic conditions, time of day, and distance.
Predicts the most probable delivery time for specific orders.
Example: A customer receives an estimated delivery time considering traffic, distance, and time of day.
Detect Viral Spam
Facebook utilizes machine learning to detect and prevent the spread of viral spam content.
ML algorithms analyze content sharing patterns, user reports, and characteristics of flagged spam.
Identifies and quarantines potentially viral spam content.
Example: Restricts visibility of a sudden surge in link sharing, preventing widespread dissemination.
Recommend Relevant Marketplace Items
Amazon Marketplace leverages machine learning to recommend relevant items.
ML algorithms analyze user interactions, purchase history, and behavior.
Suggests items aligning with the user's preferences.
Example: Recommends relevant office supplies based on browsing and purchase history.
Forecast Demand in Fashion E-commerce
ASOS uses machine learning to forecast demand for fashion items.
ML models analyze historical sales data, browsing behavior, fashion trends, and seasonal patterns.
Predicts the demand for specific fashion items.
Example: Predicts increased demand for winter jackets based on dropping temperatures.
Recommend Interesting Tweets
Twitter uses machine learning to recommend tweets tailored to individual users' interests.
ML models analyze user interactions, tweet content, and engagement patterns.
Suggests tweets aligning with user preferences.
Example: Recommends tweets about camera tips to a user passionate about photography.
Generate Queries with Natural Language
Google Search employs machine learning to generate search queries using natural language.
NLP models analyze user input, language structure, and context.
Generates search queries aligned with user intent.
Example: Suggests nearby hiking trails based on a user's natural language query.
Organize E-commerce Content
Etsy utilizes machine learning to organize e-commerce content using embeddings.
ML models use embeddings to categorize and group similar products.
Based on attributes and user preferences.
Example: Handmade jewelry gets organized into categories like "Vintage Inspired" based on product descriptions.
Personalized Listing Search
Airbnb applies machine learning to personalize listing search results for users.
ML algorithms analyze user search history, preferences, past bookings, and listing attributes.
Personalizes and ranks search results.
Example: Optimizes search results highlighting pet-friendly accommodations for a user favoring such listings.
Select Relevant Marketing Messages
Mailchimp applies machine learning to select relevant marketing messages for email campaigns.
ML algorithms analyze user behavior, demographics, and past engagement.
Tailors and selects relevant marketing messages for each recipient.
Example: Sends personalized emails promoting sports equipment to users interested in fitness-related products.
Detect Patterns in Text Data
Twitter uses machine learning to detect patterns in text data, identifying trends, sentiments, or anomalies.
ML models analyze textual data through natural language processing and pattern recognition.
Identifies trends, sentiments, and emerging topics.
Example: Identifies a surge in tweets with a specific hashtag related to breaking news.
Select Best Payment Gateway
Shopify leverages machine learning to select the best payment gateway for merchants.
ML algorithms analyze transaction data, customer preferences, and payment gateway performance.
Suggests the ideal option for each merchant.
Example: Recommends a payment gateway based on business type and previous transaction data for a new merchant.
Predict New Product’s Sales Potential
Walmart utilizes machine learning to predict the potential sales of new products.
ML models analyze historical sales, product attributes, demographics, and market trends.
Forecasts the potential sales volume of new products.
Example: Predicts high demand for a new electronic gadget by analyzing its features and similarities to successful products.
Predict If a Store Is Open
Google Maps employs machine learning to predict whether a store or business is open.
ML algorithms process historical business hours, user-generated data, and real-time information.
Predicts open/closed status for businesses.
Example: Indicates real-time information that a restaurant is currently open.
Identify Business Customers
Salesforce uses machine learning to identify potential business customers for its clients.
ML algorithms analyze company profiles, industry data, purchasing behavior, and client preferences.
Identifies potential business customers.
Example: Provides a client with a list of potential leads based on industry, size, and location.
Detect Fraud with Embeddings
PayPal leverages machine learning with embeddings to detect fraudulent transactions.
ML models use embeddings to represent transaction patterns and user behavior.
Flags potential fraud based on anomalous activities.
Example: Flags a transaction as potential fraud based on unusual spending behavior.
Improve Travel Search Experience
Expedia uses machine learning to enhance the travel search experience for users.
ML algorithms analyze user preferences, past bookings, travel trends, and accommodation details.
Offers tailored and optimized travel recommendations.
Example: A user searching for a beach vacation receives curated travel packages based on preferences.
Predict Availability of Food Items
Instacart utilizes machine learning to predict the availability of food items at partner stores.
ML algorithms analyze inventory data, purchase patterns, and real-time updates.
Predicts the availability of food items in partner stores.
Example: Indicates "Limited Stock" for avocados based on real-time updates from partner stores.
Personalize the Homepage Feed
Netflix applies machine learning to personalize the homepage feed for users.
ML algorithms analyze user viewing history, preferences, ratings, and browsing behavior.
Prioritizes and personalizes content on the homepage.
Example: A user interested in crime documentaries sees a personalized feed showcasing relevant content.
Forecast Order Volumes and Deliveries
Amazon uses machine learning to forecast order volumes and delivery demands.
ML algorithms analyze historical order data, seasonal trends, customer behavior, and external factors.
Predicts orders and plans delivery logistics.
Example: Predicts a surge in orders before a major sale event, allocating resources for efficient customer service.
Forecast Flight Prices
Google Flights employs machine learning to forecast flight prices for users.
ML models analyze historical pricing, booking patterns, seasonality, and external factors.
Predicts future flight prices accurately.
Example: Notifies a traveler of a price increase, prompting them to book based on predicted future fluctuations.
Generate Engaging Email Subject Lines
Mailchimp uses machine learning to create engaging email subject lines.
ML algorithms analyze email content, user behavior, and engagement metrics.
Generates subject lines optimized for higher open rates.
Example: Suggests subject lines based on successful patterns, increasing open rates for email campaigns.
Figure Out Users' Preferences
Netflix leverages machine learning to understand users' content preferences.
ML algorithms analyze user viewing history, ratings, time spent on content, and genre preferences.
Suggests personalized content recommendations.
Example: A user receives tailored content recommendations based on their specific preferences.
Identify Objects in Images
Google Photos uses machine learning to identify objects in images.
ML models analyze image data through computer vision.
Detects and labels objects for image categorization and search.
Example: Automatically labels uploaded images with tags like "beach" or "dog."
Identify and Block Unwanted Callers
Truecaller applies machine learning to identify and block unwanted callers.
ML algorithms analyze call patterns, user reports, and caller information.
Identifies and flags unwanted calls based on known spam patterns.
Example: Automatically identifies and blocks spam calls based on reported numbers.
Suggest Relevant Search Queries
Google Search employs machine learning to suggest relevant search queries.
ML models analyze user search history, context, and browsing behavior.
Generates relevant search query suggestions in real-time.
Example: Suggests completed search queries as a user begins typing, aligning with their intent.
Generate Code and Code Suggestions
GitHub applies machine learning to generate code and provide coding suggestions to developers.
ML models analyze code repositories, programming patterns, and best practices.
Generates code snippets and offers suggestions for improvement.
Example: A developer receives coding suggestions, optimizing algorithms and identifying potential bugs.
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).
Subscribe today!
Subscribe and Start today!!
Github : https://bit.ly/3jFzW01
Learn how to efficiently use Python Built-in Data Structures
Let’s get started with new system design case studies-
More ML system design case studies coming soon! Follow - Link
Thanks,
Team Ignito