We are excited to share that Fiddler has been named a Sample Vendor for Explainable AI in two 2021 Gartner Hype Cycle reports—the Hype Cycle for Data Science and Machine Learning, 2021 and the Hype Cycle for Analytics and Business Intelligence, 2021.
The Hype Cycle for Data Science and Machine Learning, 2021 states “accelerated digitization is driving the urgency to productize experimental data science and machine learning initiatives. Data and analytics leaders must analyze the evolution of existing and emerging trends to orchestrate and productize DSML”.. The Hype Cycle for Analytics and Business Intelligence, 2021 “helps data and analytics leaders evaluate the maturity of innovations across the ABI space”.
Gartner describes Explainable AI as “a set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior, and identifies any potential biases.”.
Fiddler provides Explainable AI as part of its Model Performance Management platform to empower its users with observability into their machine learning models. With cutting-edge AI explainability techniques like Shapley Values and Integrated Gradients, which Fiddler has contributed research towards, model practitioners and stakeholders get insight into why a model behaved the way it did and how each feature contributed to the outcome, either for single predictions or across an entire segment of the data. In fact, our Slice and Explain™ enables practitioners to drill down and analyze model behaviors faster using a familiar SQL query.
Most AI systems are a black box. However, Fiddler is built for performance scale to give teams visibility into every stage of model development and create a culture of accountability. To learn more about how Fiddler can help your team build trust with AI, request a demo.
 Gartner, “Hype Cycle for Data Science and Machine Learning”, Farhan Choudhary, Alexander Linden, Jim Hare, Pieter den Hamer, Shubhangi Vashisth, August 2, 2021.
 Gartner, “Hype Cycle for Analytics and Business Intelligence”, Austin Kronz, Peter Krensky, July 29, 2021.
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