sagemaker-studio-auto-shutdown-extension VS amazon-sagemaker-examples

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.

I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb

I'm trying to define a step scaling policy for my sagemaker realtime endpoint, based on this example notebook. I understand that the step scaling policy defines thresholds to provision a different amount of instances, but I am confused because it doesn't seem to specify the metrics to track.

Yeah man: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb

It's time to get your hands dirty by solving some ML Use Cases of your own from AWS SageMaker Use Cases repo.

Additionally, you can easily deploy pretrained fastText models on their own to live SageMaker endpoints to compute embedding vectors on the fly for use in relevant word-level tasks. See the following GitHub example for more details.

We are kind of boxed into using Sagemaker at our organization and we need to do a POC for Sagemaker's model monitoring. We noticed that Sagemaker monitoring works best with models that use tabular data/features. There are a lot of example notebooks that demonstrate model monitoring capabilities, but all of the examples are based on tabular data. We are trying to apply Sagemaker's model monitoring and gather metrics from Data Quality, Model Quality, Bias Drift, Feature Attribution Drift, and Explainability and then push those metrics into CloudWatch, similar to what was done in these notebooks: https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_model_monitor .

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.

Amazon SageMaker provides four new tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular and TabTransformer. These popular, state-of-the-art algorithms can be used for both tabular classification and regression tasks. They are available through the SageMaker JumpStart UI inside of SageMaker Studio, as well as through python code using SageMaker Python SDK. To learn how to use these algorithms, you can find SageMaker example notebooks below:

In this section, we go through an example in which we show you how to compile a BERT model with Neo for AWS Inferentia. We then deploy that model to a SageMaker endpoint. You can find a sample notebook describing the whole process in detail on GitHub.