Update your algorithms with confidence
ML models evolve fast. Either you are using third party or custom, in-house built algorithms, new features have to be included quickly in order to stay on top of the competition. Such a short release cycle requires an efficient test framework to verify that the algorithm quality increases over time.
Typical regression tools are not a good fit when verifying model quality. This lack of functionality leads to either superficial testing or very time consuming manual procedures. The monitoring functionality of Yields can also be used to compare different versions of a model, to detect possible regression issues and quantify the progress. With Yields, you will know for the impact of a version upgrade before you push the algorithm to production.
Who is it for
Project managers who need to de-risk analytics centered projects.
Managers seeking to measure and report progress on quantitative models
Businesses looking to shorten their analytics development cycle
QA testing teams who want to boost their throughput
Benefits to your business
Faster analytics test cycles leading to more frequent releases and shorter time to market
Parallel detection of algorithmic regression issues
Full reproducibility of discovered problems
Integral part of your continuous integration workflow
One central location where all the test data is available