For enterprise businesses, applying advanced machine learning (ML) techniques like deep learning can deliver transformative business outcomes, yet the black-box nature of these approaches creates barriers of understanding that can slow adoption to a halt. ML model interpretability, or the ability to explain why and how a model makes a prediction, can enable business stakeholders to quickly understand the how and why of predictive outcomes and confidently make decisions that optimize for future business results
Discover best practices for building and deploying interpretable ML models at scale
Learn how Cloudera Machine Learning’s model ops and interactive applications functionalities deliver business-ready predictive apps for business users
Explore an in-depth technical guide to ML interpretability and working application prototypes from Cloudera Fast Forward Labs
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