Scalable Feature Engineering with LanceDB's Geneva and Ray скачать в хорошем качестве

Scalable Feature Engineering with LanceDB's Geneva and Ray

Learn how to do scalable feature engineering with LanceDB and Geneva. This demo shows how to enrich the Oxford Pets dataset with file size, image dimensions, GPU-generated captions, and vector embeddings. The jobs are running distributed on a Ray cluster for high-performance machine learning pipelines. This demo is quite complex, so we encourage you to read the article first! The steps outlined in the blog will help guide you through the tutorial: https://lancedb.com/blog/geneva-featu... You can find all the code in this tutorial in our Python notebook. We ran this from Google Vertex AI, so you will have to setup your own machine: https://colab.research.google.com/git... 🔗 Try LanceDB → https://lancedb.com ⭐️ Check out LanceDB repo → https://github.com/lancedb 🗣️ Join LanceDB community →   / discord   📱 LinkedIn →   / lancedb   | Twitter → https://x.com/lancedb