AWS machine learning talk

I enjoyed giving my “Intro to Amazon Machine Learning” talk at the AWS Denver Boulder meetup.   (Shout out to an old friend and colleague who came out to see it.) I didn’t get through the whole pipeline demonstration (I didn’t get a chance to do the batch prediction), but the demo gods were kind and the demo went well.

We also had a good discussion.  A few folks present had used machine learning before, so we talked about where AML made sense (hint, it’s not a fit for every problem).  Also had some good questions about AML, about performance and pricing.  One of the members shared a reinvent anecdote: the AML team looked at all the machine learning used in Amazon and graphed the use cases and solved for the most common ones.

As, usual, I also learned something. OpenRefine is a tool to help you prepare data for machine learning.  And when you change the score cut-off, you need to restart your real-time end point.

The “Intro to Amazon Machine Learning” slides are up on SlideShare, and big thanks to the Meetup organizers.



From my book, Amazon Machine Learning: An Introduction:

Amazon Machine Learning, or AML, provides you access to widely applicable machine learning algorithms without having to run any servers.  This type of learning is useful for making predictions based on a set of data for which answers are known.  AML supports supervised learning with the stochastic gradient descent algorithm.  The end goal of AML is to create a model, which is what will allow you to make further predictions based on past data.

AML supports three different kinds of predictions.  For binary outcomes, where observations lead to a yes/no result, AML supports binary classification.  An example would be whether or not a prospect is likely to sign up for a new account, given their past interactions with your company.  For multi valued results, where observations lead to one of N results, AML supports multi class classification.  A good example of this would be which product to show a customer, given what they’ve looked at and bought in the past.  And, for numeric values, AML supports regression.  An example of that would be predicting house prices based on sales data and house attributes.

If you are not trying to use existing data and create predictions out of it using supervised learning, but are trying to instead recognize images or tease out patterns in text, you may want to consider alternatives to AML.

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Amazon Machine Learning Video and Book

I’m working on a video series and an ebook about Amazon Machine Learning, or AML.

AML  is a great way to get started with machine learning, since you can focus on the key concepts of building and using a model and not worry about any infrastructure.  AWS takes care of provisioning all the underlying IT infrastructure–you just worry about getting your data to S3, choosing how to build the model, and then using the model.  Which, trust me, is quite enough to tackle if you are a machine learning newbie.

You can use the model to get predictions either in real time (with a default soft limit of 200 requests per second) or via batch processing, where you can upload up to 1TB of predictions to S3.  Like everything in AWS, you can control the entire process via a well documented API or from various SDKs.

AML isn’t a fit for all machine learning needs–it processes text that is in CSV format and supports only supervised learning.  There are other options on AWS (and other places as well).

The book is currently in progress, and I’ll be starting on the video soon.If you’d like to follow along as the book gets written, you can at leanpub: Amazon Machine Learning: An Introduction.



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