Machine learning: In-memory computing for continuous learning

Using In-Memory Computing for Continuous Machine and Deep Learning

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One of the greatest challenges you’ll face in machine learning implementation isn’t choosing algorithms themselves - but training those algorithms to become accurate and reliable.

This continuous learning is an intensive process and data must move quickly in and out of algorithms to ensure results are always improving.

In-memory computing has been used for the last decade:

  • To add speed and scale to existing applications
  • To ingest massive datasets in real-time
  • To perform real-time analytics and high-performance computing

Continuous machine and deep learning could be 1 more area that in-memory computing excels in - read this white paper to find out more.

Vendor:
GridGain
Posted:
04 Apr 2019
Published:
31 Dec 2018
Format:
PDF
Type:
White Paper
Language:
English
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