Machine Learning Engineer System Design Interview Questions

Million D. - "Machine learning software engineer interviews at Google are really challenging. The questions are difficult, specific to Google, and they cover a wide range of topics."See full answer

Adam L. - "Discussed: Requirements of the system: latency language modality (assume keyboard typing) availability of data (assume cold start) success metric (accuracy of next word predicted?, or minimize false positives? -> accuracy to start) Data collection and processing: design ethical user experiments to collect typed out data design a simple tokenization strategy (word level encoding, character level encoding, byte-pair encodings, and discuss tradeoffs) collect data, and split"See full answer

Ajay P. - "Designing an evaluation framework for ads ranking is crucial for optimizing the effectiveness and relevance of ads displayed to users. Here's a comprehensive framework that you can use: Define Objectives and Key Performance Indicators (KPIs):** \\Click-Through Rate (CTR):\\ The ratio of clicks to impressions, indicating the effectiveness of an ad in attracting user attention. \\Conversion Rate:\\ The ratio of conversions (e.g., sign-ups, purchases) to clicks, measuring how well"See full answer

Jacky Y. - "Problem scope: Can this system detect Bot in real-time online or offline? Both. Online traffic: 1M DAU. Latency: 2s. Offline frequency: daily Offline data: 2B activity logs. Data: How do we know a Bot player (Label)? Human label. Imbalance data: reweight, resample. Develop a Bot simulator to generate more data offline for training. Given lower weight to simulator data than human label. Features: Signals from different models online. Log all the features for offline. Propose new features: dail"See full answer

Kartikeya N. - "C : Okay. So I would want to start with knowing what is the product for which we have to build a recommendation system. I : This is a photo sharing product. C : Okay. So is this something on the lines of Instagram? I : Yes C : Okay. And are we a new product co or we have some current product built already? I : You can assume yourself. C : Okay. Is there any demography or country we are targeting? I : No, this is a global product C : Okay. So, the biggest goal of any product recommendation system"See full answer

Sai vuppalapati M. - "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"See full answer

Scott S. - " Functional Requirements Content Ingestion\: Ingest news articles from various sources (websites, social media, etc.). Handle different types of content (text, images, videos). Content Analysis\: Extract and preprocess text from articles. Analyze the content for potential indicators of fake news. Model Training and Prediction\: Use machine learning models to classify content as fake or real. Continuously improve models with new data and f"See full answer

Deepak K. - "FN Given text need to figure out is it following guidelines. Should notify the user in case of not following guidelines. Reason for failure should have misleading/spam/adult filters. NFN High availability High Scalability Low latency of processing Estimations 1M requests/min text - 10kb => 9.5GB/min => 14TB/day API fetchmoderationscore(text) score will be between 0 to 1 more than 0.8 => not following guidelines fetchmoderationscore(text, filter)"See full answer

Anonymous Hare - "Sharing the approach for functional requirements we tool to solve this question. Functional Requirements This is only for the Registered users What is a "For You" page ? Home page where you get suggestions based on people you follow. Interactions like/share/comments (done by user) Interests (shared by the user during registration or onboarding) sports choices/ region choices/ Video sharing platform. So how many videos should we s"See full answer

Satyajit G. - "It might not be a good idea to predict stock prices only based on reddit comments. You could create a signal from reddit comments that can indicate "social media interest" and feed it into a ML system (along with other features) that predicts prices. Collecting good data to train the model and evaluating it correctly are going to be huge challenges."See full answer