From the course: Building Recommender Systems with Machine Learning and AI

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SageMaker in action: Factorization machines on one million ratings, in the cloud

SageMaker in action: Factorization machines on one million ratings, in the cloud - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

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SageMaker in action: Factorization machines on one million ratings, in the cloud

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- [Instructor] For this activity just watch. Doing this yourself would involve spending real money and it's a little too easy to forget to shut things down when you're done and end up with a huge AWS bill you didn't expect. I've already created a SageMaker notebook and I've started that in the SageMaker consol in AWS so we can take a look at it. It will take a minute or so for the notebook environment to spin up before we can launch it. Once the notebook environment is up we can create new notebooks or open up ones we created earlier. Let's open up my movie lens dash one m notebook here. This is loosely based on a similar example in the AWS blog but I've simplified it a bit and modified it to work with the one million rating data set let's restart the kernel so we have a clean slate to work with. Alright, the first thing we need to do is download the movie lens data set and decompress it, so we'll use a little trick in notebooks where you can use the exclamation mark to execute shell…

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