lit VS amazon-sagemaker-examples

I'd also suggest looking at your data sample, and how your model handles it, with some kind of exploratory analysis tool. Google's Language Interpretability Tool might work for your scenario. This can give you a lot of ideas about how to prepare the data better.

Visualize and understand NLP models with the Language Interpretability Tool The Language Interpretability Tool (LIT) is for researchers and practitioners looking to understand NLP model behavior through a visual, interactive, and extensible tool. Use LIT to ask and answer questions like: What kind of examples does my model perform poorly on? Why did my model make this prediction? Can it attribute it to adversarial behavior, or undesirable priors from the training set? Does my model behave consistently if I change things like textual style, verb tense, or pronoun gender? LIT contains many built-in capabilities but is also customizable, with the ability to add custom interpretability techniques, metrics calculations, counterfactual generators, visualizations, and more.

Use LIT https://github.com/PAIR-code/lit

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.