GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Curated papers, articles, and blogs on data science & machine learning in production. ⚙️ Figuring out how to implement your ML project? Learn how other organizations did it: How the problem is framed 🔎(e.g., personalization as recsys vs. search vs. sequences) What machine learning techniques worked ✅ (and sometimes, what didn't ❌) Why it works, the science behind it with research, literature, and
How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions
How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions When comes to machine learning, data is certainly the new oil. The processes for managing the lifecycle of datasets are some of the most challenging elements of large scale machine learning solutions. Data ingestion, indexing, search, annotation, discovery are some of the aspects r
Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest - Keen
Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest Here at Keen IO, we believe that companies who learn to wield event data will have a competitive advantage. That certainly seems to be the case at the world’s leading tech companies. We continue to be amazed by the data engineering teams at Facebook, Amazon, Airbnb, Pinterest, and Netflix. Their work sets new standards
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The 10 main takeaways from MLconf SF
