Resources

David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, and Krishnaram Kenthapadi. 2022. "Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models": arXiv:2111.13657.

Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, and Tobias Meisen. 2019. "Ablation Studies in Artificial Neural Networks": arXiv:1901.08644.

Brian Babcock, Mayur Datar, and Rajeev Motwani. "Sampling From a Moving Window Over Streaming Data": Princeton University.

Chip Huyen. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. Sebastopol, California: O'Reilly Media, 2022.

Cathy Chen, Niall Murphy, Kranti Parisa, D. Sculley, and Todd Underwood. Reliable Machine Learning: Applying SRE Principles to ML in Production. Sebastopol, California: O'Reilly Media, 2022.

AWS Well-Architected Framework: Machine Learning Lens

Detect NLP data drift using custom Amazon SageMaker Model Monitor (blog post)

Dynamic A/B testing for machine learning models with Amazon SageMaker MLOps projects (blog post)

Amazon SageMaker

Data Version Control (DVC)

AWS Professional Services