Our great sponsors
-
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Also, you can use provided CDK stack to deploy network resources automatically. Once completed, you would have such network:
Sometimes people can forget to shut down instances after they finished their work. This can lead to the situation when resources run all night and in the morning you will receive unexpectedly large bill. But one of JupyterLab extensions can help you to avoid such situations. It automatically shuts down KernelGateway Apps, Kernels and Image Terminals in SageMaker Studio when they are idle for a stipulated period of time. You will be able to configure an idle time limit of your preference. Instructions for setup and scripts can be found in GitHub repository. After installation, you will see a new tab on the left of SageMaker Studio interface.
Amazon SageMaker provides XGBoost as a built-in algorithm and data science team decided to use it and re-train the model. So, data scientists just need to call built-in version and provide path to data on S3, more detailed description can be found in documentation. Example notebook can be found here.