Automated monitoring of machine learning models in production. Tracks and finds discrepancies in features, predictions, and labels
Project description
Using Concordia, you should be able to rapidly have confidence in your shipped ML models.
If everything’s working as expected, you should be able to see that, and heave a sigh of relief.
If things are not going according to plan, again, you should be able to see that rapidly, and nearly as quickly see the root cause of those discrepancies.
It’s designed to work across environments, with many critical parameters configurable (such as database settings).
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
concordia-0.0.1.tar.gz
(7.4 kB
view hashes)
Built Distribution
Close
Hashes for concordia-0.0.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1ccd0952f94740551395656bfb8ebbc640537a11be1ac4a6290836e97e74f482 |
|
MD5 | 1bfb894e0306240b829c9edb521a5acf |
|
BLAKE2b-256 | 34143f8d4eb8df0fad2c48820af120e597bac614e9fa84f40d79660ac25ca91a |