The system design library
Search and filter every curated system design video, article, repository and paper in one place.
76 resources
Accelerator: Fast Data Processing Framework at eBay
The Expertmaker Accelerator is a well-proven data processing framework that provides fast data access, parallel execution, and automatic organization of source code, input data, and results. It can be used for daily data analysis tasks a…
Case StudiesIntermediateBy eBay engineers · eBay
Adobe Research
Look into the future of innovation at Adobe Research
BlogsIntermediateBy Adobe engineers · Adobe
Altus Care: Applying a Chatbot to Platform Engineering at eBay
eBay developers recently combined several commonly used technologies, including Lucene search and the Stanford Natural Language Processor, to create Altus Care, a chatbot that provides an instant one-stop support solution for internal eB…
Case StudiesIntermediateBy eBay engineers · eBay
Amazon Science
Technical deep-dives and perspectives from our scientists who are pushing boundaries across multiple scientific disciplines while solving real-world challenges. Explore how our teams combine research excellence, world-class data, and adv…
BlogsIntermediateBy Amazon engineers · Amazon
Analytics Pipeline at Lyft
A real-world engineering write-up from Lyft on big Data.
PapersIntermediateBy Lyft
Anthropic Blog
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
BlogsIntermediateBy Anthropic engineers · Anthropic
Apple Machine Learning Blog
Apple machine learning teams are engaged in state of the art research in machine learning and artificial intelligence. Learn about the latest advancements.
BlogsIntermediateBy Apple engineers · Apple
Architecting Restaurant Wait Time Predictions at Yelp
Architecting Restaurant Wait Time Predictions Shweta Joshi, Machine Learning Engineer Dec 12, 2019 Is there a restaurant you’ve always wanted to check out, but haven’t been able to because they...
Case StudiesIntermediateRead hereBy Yelp engineers · Yelp
Aroma: Using ML for Code Recommendation at Facebook
Thousands of engineers write the code to create our apps, which serve billions of people worldwide. This is no trivial task—our services have grown so diverse and complex that the codebase contains…
Case StudiesIntermediateRead hereBy Facebook engineers · Facebook
Atom Smashing using Machine Learning at CERN
A real-world engineering write-up from CERN on distributed Machine Learning.
PapersIntermediateBy CERN
Automated Fake Account Detection at LinkedIn
To maintain a safe and trusted professional community on LinkedIn, we require that every LinkedIn profile must uniquely represent a real person. One of the ways we ensure that accounts are real is by building automated detection systems …
Case StudiesIntermediateRead hereBy LinkedIn engineers · LinkedIn
AWS Machine Learning Blog
It’s our goal for AWS to be the most secure place to run any workload, and in support of that we’ve been deeply investing in security across our services since AWS’s inception more than two decades ago. Our AI services like Amazon Bedroc…
BlogsIntermediateBy Amazon Web Services engineers · Amazon Web Services
Big Data Analytics and ML Techniques at LinkedIn
A real-world engineering write-up from LinkedIn on big Data.
PapersIntermediateBy LinkedIn
Big Data Processing (2 parts) at Spotify
In this part we’ll take a closer look at Scio, including basic concepts, its unique features, and concrete use cases here at Spotify.
Case StudiesIntermediateRead hereBy Spotify engineers · Spotify
Big Data Processing at Uber
A real-world engineering write-up from Uber on big Data.
PapersIntermediateBy Uber
Box Graph: Spontaneous Social Network at Box
By now it's no secret that the amount of content that is being created and shared is increasing exponentially. To help extract value from all of that content, we introduced Box Skills, a framework that brings machine learning innovations…
Case StudiesIntermediateRead hereBy Box engineers · Box
CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo
yahoohadoop: By Andy Feng(@afeng76), Jun Shi and Mridul Jain (@mridul_jain), Yahoo Big ML Team Introduction Deep learning (DL) is a critical capability required by Yahoo product teams (ex. Flickr, Image Search) to gain intelligence from …
Case StudiesIntermediateBy Yahoo engineers · Yahoo
Cannes: ML saves $1.7M a year on document previews at Dropbox
Recently, we translated the predictive power of machine learning (ML) into $1.7 million a year in infrastructure cost savings by optimizing how Dropbox generates and caches document previews. Machine learning at Dropbox already powers co…
Case StudiesIntermediateRead hereBy Dropbox engineers · Dropbox
Clustering Similar Stories Using LDA at Flipboard
There is more to a story than meets the eye, and some stories deserve to be presented from more than just one perspective. With Flipboard 4.0, we have released story roundups, a new feature that adds coverage from multiple sources to a s…
Case StudiesIntermediateRead hereBy Flipboard engineers · Flipboard
Cognition AI Blog
How an autonomous software-engineering agent is built, evaluated and run in production.
BlogsIntermediateBy Cognition
Concourse: Generating Personalized Content Notifications in Near-Real-Time at LinkedIn
Social media are computer-mediated platforms that facilitate creation and sharing of information, ideas, career interests, and other forms of expression via virtual communities and networks. In the last few years, notifications have been…
Case StudiesIntermediateRead hereBy LinkedIn engineers · LinkedIn
Cross-Lingual End-to-End Product Search with Deep Learning at Zalando
How We Built the Next Generation Product Search from Scratch using a Deep Neural Network
Case StudiesIntermediateRead hereBy Zalando engineers · Zalando
Cursor Blog
Research and engineering write-ups on building an AI code editor, from indexing to agents.
BlogsIntermediateBy Cursor · Anysphere (Cursor)
Data Infrastructure at LinkedIn
Shirshanka Das describes LinkedIn’s Big Data Infrastructure and its evolution through the years, including details on the motivation and architecture of Gobblin, Pinot and WhereHows.
Case StudiesIntermediateBy LinkedIn engineers · LinkedIn