Learn about ML model evaluation, monitoring and MLOps with our in-depth guides.
Want to learn production ML monitoring? Sign up for our Open-source ML observability course for data scientists and ML engineers. It's free!
How to use Precision and Recall at K to assess the performance of ranking and recommender systems.
How to compute MAP to evaluate the quality of ranking and recommender systems.
How to use NDCG at K to measure the ranking quality for both binary and graded relevance scores.
We ran an experiment to help build an intuition on how popular drift detection methods behave. In this guide, we share the key takeaways and the code to run the tests on your data.