Implementing analytics on AWS SageMaker

AWS SageMaker is a fully-managed service that enables data scientists to build, train, and deploy ML models at any scale. AWS SageMaker is based on Jupyter Notebook, so that developers can use a familiar user interface to build their own analytics. The basic concepts of SageMaker are the same as Azure ML. We can build our analytics on Jupyter and our training cluster through a Python API, and then deploy our model as a web app that can be consumed through a REST API. SageMaker also supports built-in algorithms to train our model. These include K-Means, K-Nearest Neighbors, Linear Learner, Neural Topic Model (NTM), Principal Component Analysis (PCA), and Random Cut Forest.

Evaluating the remaining useful life (RUL) of an engine with SageMaker

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