Monitoring AWS Sagemaker models with DataRobot MLOps

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DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production. You can deploy models to the production environment of your choice and continuously monitor the health and accuracy of your models, among other metrics.
AWS Sagemaker is a fully managed service that allows data scientists and developers to build, train, and deploy machine learning models.

DataRobot MLOps with its AWS Sagemaker integration provides an end-to-end solution for managing machine learning models at scale. You can easily monitor the performance of your machine learning models in real-time, and quickly identify and resolve any issues that arise.


About this Accelerator

This notebook walks you through the steps to train and host a SageMaker model that can be monitored in the DataRobot platform. 

It demonstrates how to build a custom SageMaker container in your local environment by including custom Python libraries for both training and inference. It also includes DataRobot-related libraries useful for model monitoring.


Once you have your container packaged, you can use it to train models and use the model for hosting.

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Last update:
‎09-05-2023 10:09 PM
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