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amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
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The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface. (by PAIR-code)
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Amazon SageMaker Asynchronous Inference with Hugging Face Model Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming requests and processes them asynchronously. SageMaker currently offers two inference options for customers to deploy machine learning models: 1) a real-time option for low-latency workloads 2) Batch transform, an offline option to process inference requests on batches of data available upfront. Real-time inference is suited for workloads with payload sizes of less than 6 MB and require inference requests to be processed within 60 seconds. Batch transform is suitable for offline inference on batches of data. This notebook provides an introduction on how to use the SageMaker Asynchronous inference capability with Hugging Face models. This notebook will cover the steps required to create an Asynchronous inference endpoint and test it with some sample requests.
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.
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