The importance of layered thinking in data engineering
Looks a bit like a data lake right? (Tangled wires by Cory Doctorow on Flickr (CC BY-SA 2.0) )Who is this for?Are you a data scientist or data engineer keen to build sustainable and robust data pipelines? Then this article is for you! We’ll walk through a real-world example and by the end of this article you’ll understand why you need a layered data engineering convention to avoid the mistakes we
Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In January 2019, Uber introduced Manifold, a model-agnostic visual debugging tool for machine learning that we use to identify issues in our ML models. To give other ML practitioners the benefits of this tool, today we are excited to announce that we have released Manif
ML Ops: Machine Learning as an Engineering Discipline
So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved
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