Learn how to define custom metrics in Amazon SageMaker training jobs with this beginners guide. This fifth article of the Amazon SageMaker series focuses on how to track custom metrics, allowing you to monitor the performance of your machine learning models and make informed decisions on how to evaluate them.
Take advantage of the cost savings available through EC2 Spot Instances using Amazon SageMaker.
Learn how to set up SageMaker Studio, install dependencies, shut down instances, and common troubleshooting tips.
This second article of the Amazon SageMaker series focuses on how to use Amazon SageMaker Inference Recommender to help choose optimal instance for endpoint deployment.
In this article, we will show you how to train a Detectron2 model for object detection on Amazon SageMaker.
Amazon SageMaker offers an easy and effective way to develop ML models at scale and removes the heavy lifting required to manage ML project complexities.
