«Trabajé con Iván en CornerJob, era mi jefe de Data. Y sin lugar a dudas puedo decir que es uno de los mejores que he tenido, sino el mejor. Fue muy paciente conmigo y siempre estaba dispuesto a ayudarme en todo, gracias a esto, pude crecer como Data Scientist y ser la persona que soy hoy en día. Lo recomiendo a cualquier empresa, además de que crea buen ambiente de trabajo y esto es fundamental en nuestro día a día entre tanto 'dato'.»
Iván Ruz Ruz
Barcelona, Cataluña, España
2 mil seguidores
Más de 500 contactos
2 mil seguidores
Más de 500 contactos
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Leader and mentor of Data Science & Engineering teams, specialized in fast growing…
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Peter W. J. Staar
🚀 New in Docling: Structured Data Extraction from Documents! 🚀 We’ve just added a brand-new functionality: extraction of structured data from complex documents using free-form schemas 🤩 What does that mean? 🔹 You can now skip the conversion step — instead of turning documents into text or JSON first, Docling directly extracts the structured fields you care about. 🔹 The requested fields are defined in a free-form schema, so you can instantly align the extraction with the schemas of your own databases. 🔹 This makes it ideal for data-pipelines that don’t need full document conversion, but do need to populate structured databases from messy, unstructured documents. ✨ It’s: 1️⃣ Super simple to use (check out the code snippet 👉 https://lnkd.in/eK6vaMEe ) 2️⃣ 100% open-source and runs fully local – no API calls needed 🙌 3️⃣ Powered by cutting-edge models from NuMind (YC S22) 4️⃣ Perfect for data-pipelines where you need to populate structured databases from documents (think invoices, Curriculum Vitae, contracts, product datasheets, etc) 5️⃣ Currently focused on PDFs and images (PNG) — support for pure text coming soon! The example below shows how easy it is to define a schema and extract structured fields directly from an invoice. 👉 Try it out, break it, send us feedback, and if you like what we’re building, don’t forget to ⭐ the repo (https://lnkd.in/d4UT-6_2)! After a long and fruitful summer, the Docling team has been cooking up many new features — and this is just the beginning! 🌟 #opensource #AI #documentAI #docling #IBM #IBMResearch
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Josue “Josh” Bogran
Databricks' has two fairly new account offerings: The "Free Edition" & "Trial Accounts". Both are good for learning how to use Databricks, but they have different intended audiences. In this brief video, Greg Kroleski from Databricks' "Money Team" explains some of the key differences between these two offerings in a very practical way. Link: https://lnkd.in/eERbd7Ni
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James Cha-Earley
🔊 Audio just became AI-native in Snowflake. AI_TRANSCRIBE is now available in Cortex — meaning you can transcribe audio files, detect speakers, and get word-level timestamps without managing any ML infra. Combine it with AI_SENTIMENT or AI_CLASSIFY and suddenly every call, podcast, or voice note becomes searchable, labelable, and insight-ready. I made a new Quickstart that walks through exactly how to set this up — including combining transcription with text and image analysis: 👉 https://lnkd.in/gyxeDdPi Can't wait to see how folks use this in production 🔥 #Snowflake #CortexAI #AITranscribe #MultimodalAI #GenAI #VoiceAI
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