#AWS SAGEMAKER CLARIFY HOW TO#
#AWS SAGEMAKER CLARIFY SERIES#

Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets.

#AWS SAGEMAKER CLARIFY SERIES#

Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You will interact closely with our customers and with the academic and research communities. You will build prototypes and innovate on new representation learning solutions. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice.ĪWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. We present our deployment results including qualitative customer feedback and a quantitative evaluation. For illustration, we discuss two customer use cases. Further, we describe the technical challenges encountered and the tradeoffs we had to make. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale.

We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. Trust in the model, supporting human decision-making, or debugging and improving modelįor additional information on Shapely values and baselines, see SHAP Baselines for Explainability.įor a guide to the Amazon SageMaker Clarify documentation, see Guide to the SageMaker Clarify Documentation.Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model.

You can use these explanations for auditing and meeting regulatory requirements, building The implementation includes a scalable implementation of SHAP (Shapley Additive Explanations) based on the concept of a Shapley value fromĬooperative game theory, which assigns each feature an importance value for a particular Understand why a model made a prediction after training, or use it to provide per-instanceĮxplanation during inference. Output, providing insights into the relevance of different features. This approach determines the contribution of individual features or inputs to the model's The Autopilot explanatory functionality uses a model-agnostic feature attribution approach. Transparency in machine learning in order to trust and interpret decisions made on model Internal stakeholders understand model characteristics. These tools can help ML engineers, product managers, and other Autopilot uses tools provided by Amazon SageMaker Clarify to help provide insights into how machine learning

Aws sagemaker clarify