In-database distributed machine learning
| Autor: | Mohammed Al-Kateb, Mani Srivastava, Sanjay Nair, Wellington Cabrera, Sandeep Singh Sandha |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
SQL
010504 meteorology & atmospheric sciences Database Artificial neural network business.industry Computer science Big data General Engineering Python (programming language) 010502 geochemistry & geophysics computer.software_genre Machine learning 01 natural sciences Bottleneck Overhead (computing) Artificial intelligence business computer 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | Proceedings of the VLDB Endowment. 12:1854-1857 |
| ISSN: | 2150-8097 |
| DOI: | 10.14778/3352063.3352083 |
| Popis: | Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and becomes a performance bottleneck for model training. In this demonstration, we give a practical exhibition of a solution for the enablement of distributed machine learning natively inside database engines. During the demo, the audience will interactively use Python APIs in Jupyter Notebooks to train multiple linear regression models on synthetic regression datasets and neural network models on vision and sensory datasets directly inside Teradata SQL Engine. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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