November 2024
Intermediate to advanced
306 pages
7h 57m
English
Content preview from Apache Spark for Machine Learning
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9
Deploying a Model
Model deployment is a crucial phase in the machine learning life cycle that bridges the gap between model development and delivering actual value through data-driven decision-making. Deployment is the process by which a machine learning model is integrated into an existing production environment to make real-time predictions based on new data. Understanding the importance of model deployment reveals its pivotal role in operationalizing data insights and achieving the practical benefits of machine learning.
This chapter focuses on several considerations and strategies for deploying a model in production. In this chapter, we’re going to cover the following main topics:
- Importance of model deployment
- Exploring ML pipelines ...
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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.
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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.