AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. In 2014, Project Jupyter evolved from IPython. There, you can use infographics, custom visualizations, and broader ways to explain your ideas.

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AWS Machine Learning Blog

JUNE 3, 2024

To learn more about how SageMaker Canvas uses training and validation datasets, see Evaluating Your Model’s Performance in Amazon SageMaker Canvas and SHAP Baselines for Explainability. About the Authors Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

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Unite.AI

DECEMBER 15, 2023

Nora Petrova, is a Machine Learning Engineer & AI Consultant at Prolific. My role at Prolific is split between being an advisor regarding AI use cases and opportunities, and being a more hands-on ML Engineer. I started my career in Software Engineering and have gradually transitioned to Machine Learning.

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