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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data FREE CHAPTER 2. Matrices, Probability, and Statistics 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Running models reliably on remote machines

Whether your company uses on--premise infrastructure or cloud infrastructure, you will need to run your machine learning models somewhere other than your laptop at some point. These models might need to be ready to serve fraud predictions, or they may need to process user-uploaded images in real time. You cannot have the model sitting on your laptop and successfully serve this information.

However, as we get our data processing and machine learning applications off of our laptops, we should ensure the following:

  1. We should not complicate our applications just for the sake of deployment and scaling. We should keep our applications simple, which will help us maintain them and ensure integrity over time.
  2. We should ensure that our applications behave like they did on our local machine, where we developed them.

If we deploy our machine learning...

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