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Antipatterns in Financial Machine Learning Operations
Using AntiPatterns to avoid MLOps Mistakes
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications.
These lessons are presented in the form of antipatterns.
Just as design patterns codify best software engineering practices, antipatternsprovide a vocabulary to describe defective practices and methodologies in financial ml operations (mlops).
Antipatterns will support better documentation of issues, rapid communication between stakeholders, and faster resolution of problems.
Nikhil Muralidhar, Sathappah Muthiah, Patrick Butler, Manish Jain, Yu Yu, Katy Burne, Weipeng Li, David Jones, Prakash Arunachalam, Hays 'Skip' McCormick, Naren Ramakrishnan
Machine learning models
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