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Command-line Tools can be 235x Faster than your Hadoop Cluster (2014)

Hacker News

He writes about ML/AI/crypto/data, leadership, and building tech teams. Adam Drake is an advisor to scale-up tech companies.

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KI-gestützte Datenanalysen als Kompass für Unternehmen: Chancen und Herausforderungen

Data Science Blog

Doch veraltete Legacy-Systeme verlängern Abfragezeiten und erschweren Echtzeitanalysen großer und komplexer Datenmengen, wie sie etwa für Machine Learning (ML) erforderlich sind. Über Exasol-CEO Martin Golombek Mathias Golombek ist seit Januar 2014 Mitglied des Vorstands der Exasol AG.

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Getting Started with AI

Towards AI

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems. 12, 2014. [3] 16, 2020. [4]

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Espresso AI: Q&A mit Mathias Golombek, CTO bei Exasol

Data Science Blog

Mit dem integrierten autoML-Tool von TurinTech können Anwender zudem durch den Einsatz von ML-Modellen die Performance ihrer Abfragen direkt in ihrer Datenbank maximieren. So gelingt BI-Teams echte Datendemokratisierung und sie können mit ML-Modellen experimentieren, ohne dabei auf Support von ihren Data-Science-Teams angewiesen zu sei.

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ML Days in Tashkent — Day 1: City Tour

PyImageSearch

Home Table of Contents ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent! This blog is the 1st of a 3-part series: ML Days in Tashkent — Day 1: City Tour (this tutorial) ML Days in Tashkent — Day 2: Sprints and Sessions ML Days in Tashkent — Day 3: Demos and Workshops ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent!

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Active learning is the future of generative AI: Here’s how to leverage it

Flipboard

These problems are why, despite the early promise and floods of investment, technologies like self-driving cars have been just one year away since 2014. As a result, the AI production gap, the gap between “that’s neat” and “that’s useful,” has been much larger and more formidable than ML engineers first anticipated.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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