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

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AWS SageMaker and factorization machines

AWS SageMaker and factorization machines - Python Tutorial

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

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AWS SageMaker and factorization machines

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- [Instructor] Also worth a mention is Amazon's SageMaker services. SageMaker is a component of Amazon Web Services and it allows you to create notebooks hosted on AWS that can train large scale models in the cloud and then vend predictions from that model from the cloud as well. It's an easy way to get some serious computing horsepower behind your recommender system in an on-demand manner. And it comes with some useful algorithms for recommender systems, too. Using SageMaker involves three steps: building your model, training your model, and deploying your model. Let's start with building. It's pretty easy to start using SageMaker. You just push a button in the AWS console to start a new notebook instance and a hosted Jupiter notebook environment will be spun up for you with access to all of SageMaker's built-in algorithms available to you. You can spin up environments that include most any deep learning framework that you want to use as well, such as TensorFlow or Apache's MXNet…

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