Building and Maintaining Internal Tools for DS/ML teams: Lessons Learned
Lessons learned on how to build and maintain inner-source tools, with a focus on data science and machine learning use-cases
De-risking Real-time ML Projects: Addressing Common Failure Modes
In this post we list practices to substantially reduce the risk of failure in real-time machine learning projects.
Dealing with Train-serve Skew in Real-time ML Models: A Short Guide
A short guide explaining how to avoid and mitigate the impacts of train-serve skew in realtime ML models.
Automatic retraining for machine learning models: tips and lessons learned
In this post we'll go over some of the lessons we learned over the last years retraining models in an automatic fashion at Nubank.
Best Practices for Real-time Machine Learning: Alerting
In this article we analyze lessons learned and best practices assembled from years of applying Machine Learning to real-life problems at Nubank.
Data Scientist x Machine Learning Engineer Roles: How are they different? How are they alike?
Nubank's take on how these two roles interact and how each plays a key part in a successful team.
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Carrers
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