Mangrove forests are an important part of a healthy ecosystem, and human activities are one of the major reasons for their gradual disappearance from coastlines around the world. Using a machine learning (ML) model to identify mangrove regions from a satellite image gives researchers an effective way to monitor the size of the forests over time. In Part 1 of this series, we showed how to gather satellite data in an automated fashion and analyze it in Amazon SageMaker Studio with interactive visualization. In this post, we show how to use Amazon SageMaker Autopilot to automate the process of building a custom mangrove classifier.
Train a model with Autopilot
Autopilot provides a balanced way of building several models and selecting the best one. While creating multiple combinations of different data preprocessing techniques and ML models with minimal effort, Autopilot provides complete control over these component steps to the data scientist,

Continue reading