Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without any code at all with Amazon SageMaker Studio. Autopilot offloads the heavy lifting of configuring infrastructure and the time it takes to build an entire pipeline, including feature engineering, model selection, and hyperparameter tuning.
In this post, we show how to go from raw data to a robust and fully deployed inference pipeline with Autopilot.
Solution overview
We use Lyft’s public dataset on bikesharing for this simulation to predict whether or not a user participates in the Bike Share for All program. This is a simple binary classification problem.
We want to showcase how easy it is to build an automated and real-time inference pipeline to classify users based on their participation in the Bike Share for All

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At FusionWeb, we aim to look at the future through the lenses of imagination, creativity, expertise and simplicity in the most cost effective ways. All we want to make something that brings smile to our clients face. Let’s try us to believe us.

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