![]() Your label config may be resulting in that "ragged nested sequences" the warning is talking about. We use label and probability parameters under predictor in the Analysis Configuration to extract those predicted labels/scores. You can also use SageMaker Clarify to analyze a customer model that is deployed to a SageMaker inference endpoint. ![]() These tools can help ML engineers, product managers, and other internal stakeholders understand model characteristics. A SageMaker Clarify processing job uses the SageMaker Clarify processing container to interact with an Amazon S3 bucket containing your input datasets. Autopilot uses tools provided by Amazon SageMaker Clarify to help provide insights into how machine learning (ML) models make predictions. This may mean if we sent N records to your model, but according to your analysis configuration, when we extract the predicted labels from your model output, the number of labels is different than N. Use SageMaker Clarify Explainability with SageMaker Autopilot. These analyses take into consideration the data, including the labels, and the predictions of a model. Experience with cloud deployment (AWS, Azure, GCP) preferred, such as building and scaling in AWS SageMaker or Azure ML Studio. Post-training bias analysis can help reveal biases that might have emanated from biases in the data, or from biases introduced by the classification and prediction algorithms. Looks like it's expecting the predicted_labels to have the same number of records of data (which is the request we sent to the model). Detect Post-training Data and Model Bias with Amazon SageMaker Clarify. Notes There are two options for records depending on the format of the model container output. To configure the processing container, job inputs, outputs, resources and. This guide shows how to specify the input dataset name, analysis configuration file name, and output location for a processing job. If the error is from that warning, the code np.array(predicted_labels, dtype=self._prediction_dtype).reshape(data.shape, -1) SageMaker Clarify deserializes the model container output for each record into a JSON compatible data structure, and then uses the EnableExplanations parameter to evaluate the data. To analyze your data and models for bias and explainability using SageMaker Clarify, you must configure a SageMaker Clarify processing job. ![]() That VisibleDeprecationWarning may not necessarily be where the error is occurring, but you may be able to tell from the stacktrace yourself if it is. “Once I have my training data set, I can an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance,” Saha explained.It's hard to tell exactly what is causing the error without a stacktrace, or more information on the inputs. He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model. How do I troubleshoot errors when I import data into my Amazon SageMaker Studio using SageMaker Data Wrangler AWS OFFICIALUpdated a year ago. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle,” Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch. “We are launching Amazon SageMaker Clarify. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models. As companies rely increasingly on machine learning models to run their businesses, it’s imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions.
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