January 2020
Intermediate to advanced
280 pages
9h 22m
English
Content preview from Machine Learning for Business
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Chapter 8. Serving predictions over the web
This chapter covers
- Setting up SageMaker to serve predictions over the web
- Building and deploying a serverless API to deliver SageMaker predictions
- Sending data to the API and receiving predictions via a web browser
Until now, the machine learning models you built can be used only in SageMaker. If you wanted to provide a prediction or a decision for someone else, you would have to submit the query from a Jupyter notebook running in SageMaker and send them the results. This, of course, is not what AWS intended for SageMaker. They intended that your users would be able to access predictions and decisions over the web. In this chapter, you’ll enable your users to do just that.
Serving tweets ...
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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.