Author: Sara Gomes, Deeper Insights
What, why and how!
These are the questions we need to answer first and foremost to understand the utilisation of an experiment tracker.
Experiment tracking is simply the process of saving all experiment information for each and every experiment you run. That means saving what kind of model you used (or the model itself), what data it was trained/tested on, the training parameters, the resulting metrics, and whatever else might be important so that every experiment can be easily reproduced.
But why should you care? Because it makes for better science! Here's how:
Now that you are completely convinced that you should implement experiment tracking in all your projects, you may be wondering: how do I do that? Well, there are several publicly available, as well as paid, tools, for this exact purpose. Here at DI we've built our own Experiment Tracker module which supports 3 different off-the-shelf tracking tools, wrapping them in an abstract API, that allows seamless conversion from one tracker to another without changing the experiment code.
We continue to add better visualisation tools to our solution, making it easier to create useful dashboards to more efficiently compare the results of different runs and tune our experiments, no matter what impossible problems we're solving!
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