It is a On-Premise Continuous Integration & Delivery for Machine Learning.
It make it easy to track the progress of a machine learning project. With Losswise, we find we spend less time worrying about the operational complexity of training models so we can focus on other things such as improving datasets or experimenting with new model architectures.
Losswise provides ML practioners with an intuitive and elegant Python API and accompanying dashboard to visualize progress within training sessions as well as across training sessions.
It’s ideal for running hyperparameter searches, model comparison experiments, as well as simply tracking one-off training scripts.
example of API implementation code.
Losswise provides real time tables showing you your latest model results, including estimates of when your models will be done training.
Sort and filter by min loss, max accuracy, or whatever you wish, to get a better understanding of how parameters affect your final results.
illustration of table and statistics and how it looks like in interface
Parameters, Images, Logs
Visualize your experiments in more ways than charts and graphs like viewing the parameters of each of your algorithms.
Using Losswise for computer vision experiments? See every image processed by your algorithm and the results.
Using the Losswise Build Runner to trigger builds on your servers? See the logs in real time.
animated gif shows haw inteface works, how you can switch tabs
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