Step 1: Launch SageMaker notebook instance and set up exercise codeįrom the SageMaker landing page, choose Notebook instances in the left panel and choose Create notebook Instance. Using this pipeline, we can pass raw input data to a single endpoint that is first preprocessed and then is used to make a prediction for a given abalone. Finally, we’ll create a pipeline that combines the data processing and model prediction steps using Amazon SageMaker Inference Pipelines. Then, we’ll use our processed data to train the Amazon SageMaker Linear Learner algorithm to predict the age of abalones. We will use the SimpleImputer, StandardScaler, and OneHotEncoder transformers on the raw abalone data these are commonly-used data transformers included in Scikit-learn’s preprocessing library that process the data into a format required by ML models. To accomplish this, we’ll first do some simple preprocessing with the Amazon SageMaker built in Scikit-learn library. Since measuring the age of abalones is a time-consuming task, building a model to predict the age of the abalone enables us to estimate the abalone’s age based on physical measurements alone, removing the need to manually measure the abalone’s age. The dataset includes various data on abalones (a type of shellfish), including sex, length, diameter, height, shell weight, shucked weight, whole weight, viscera weight, and age. In our example (which is also published on GitHub), we’ll use the abalone dataset from the UCI machine learning repository. We’ll also show how you can make your ML workflow modular and reuse the preprocessing code between training and inference to reduce development overhead and errors. We’ll deploy both the library and the algorithm on the same endpoint using the Amazon SageMaker Inference Pipelines feature so you can pass raw input data directly to Amazon SageMaker. In this blog post, we’ll show how you can use the Amazon SageMaker built-in Scikit-learn library for preprocessing input data and then use the Amazon SageMaker built-in Linear Learner algorithm for predictions.
This is because most ML models expect the data in a predefined format, so the raw data needs to be first cleaned and formatted in order for the ML model to process the data.
However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale.
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