May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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16. Software Fundamentals
16.1 Introduction
We just went over a number of hardware bottlenecks you should take into account while you work on applications and data science pipelines. In this chapter, we’ll talk about important bottlenecks at the software level with regard to how data is stored.
Each of the following topics is an additional consideration to make as you’re designing your pipeline. Looking at your use case as it relates to these concepts will help you choose a more optimal storage engine as it relates to your specific use case.
16.2 Paging
In Chapter 15 we touched on how the inefficient storage of information on disk relates to overhead in retrieving that data. But how does that affect your application? Page/block size is well ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.