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You're reading from  The Definitive Guide to Google Vertex AI

Product typeBook
Published inDec 2023
PublisherPackt
ISBN-139781801815260
Edition1st Edition
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Authors (2):
Jasmeet Bhatia
Jasmeet Bhatia
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Jasmeet Bhatia

Jasmeet is a Machine Learning Architect with over 8 years of experience in Data Science and Machine Learning Engineering at Google and Microsoft, and overall has 17 years of experience in Product Engineering and Technology consulting at Deloitte, Disney, and Motorola. He has been involved in building technology solutions that focus on solving complex business problems by utilizing information and data assets. He has built high performing engineering teams, designed and built global scale AI/Machine Learning, Data Science, and Advanced analytics solutions for image recognition, natural language processing, sentiment analysis, and personalization.
Read more about Jasmeet Bhatia

Kartik Chaudhary
Kartik Chaudhary
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Kartik Chaudhary

​Kartik is an Artificial Intelligence and Machine Learning professional with 6+ years of industry experience in developing and architecting large scale AI/ML solutions using the technological advancements in the field of Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. Kartik has filed 9 patents at the intersection of Machine Learning, Healthcare, and Operations. Kartik loves sharing knowledge, blogging, travel, and photography.
Read more about Kartik Chaudhary

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Explainable AI techniques

Different techniques are available to cater to various types of data, including tabular, image, and text data. Each data type presents its own set of challenges and complexities, requiring tailored methods to provide meaningful insights into the decision-making processes of ML models. This subsection will list various XAI techniques applicable to tabular, image, and text data. The following section will dive into the ones available as out-of-the-box features in Google Cloud.

Global versus local explainability

Explainability can be categorized into two categories: local and global explainability. These terms are sometimes referred to as local and global feature importance:

  • Global explainability focuses on the overall impact of a feature on the model. This is usually obtained by calculating the average feature attribution values over the entire dataset. A feature with a high absolute value indicates that it significantly influenced the model’...
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The Definitive Guide to Google Vertex AI
Published in: Dec 2023Publisher: PacktISBN-13: 9781801815260

Authors (2)

author image
Jasmeet Bhatia

Jasmeet is a Machine Learning Architect with over 8 years of experience in Data Science and Machine Learning Engineering at Google and Microsoft, and overall has 17 years of experience in Product Engineering and Technology consulting at Deloitte, Disney, and Motorola. He has been involved in building technology solutions that focus on solving complex business problems by utilizing information and data assets. He has built high performing engineering teams, designed and built global scale AI/Machine Learning, Data Science, and Advanced analytics solutions for image recognition, natural language processing, sentiment analysis, and personalization.
Read more about Jasmeet Bhatia

author image
Kartik Chaudhary

​Kartik is an Artificial Intelligence and Machine Learning professional with 6+ years of industry experience in developing and architecting large scale AI/ML solutions using the technological advancements in the field of Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. Kartik has filed 9 patents at the intersection of Machine Learning, Healthcare, and Operations. Kartik loves sharing knowledge, blogging, travel, and photography.
Read more about Kartik Chaudhary