Two Tools Every Data Scientist Should Use for Their Next ML Project
How Uber’s Manifold and Weights & Biases’ model tracking tool can help evaluate and improve the quality of your next machine learning project.
by Braden Riggs and George Williams (gwilliams@gsitechnology.com)
The more time you spend working with machine learning models the more you realize how important it is to properly understand exactly what your model is doing and how well it is doing it. In practice, keeping track of how your model is performing, especially when testing a variety of model parameter combinations, can be tedious in the best of circumstances. In most cases I find myself building my own tools to debug and analyze my machine learning models.
Recently while working on a slew of different models for MAFAT’s doppler-pulse radar classification challenge, read more here, I found myself wasting time manually building these debugging tools. This was especially tedious as I was working on building an ensemble, a collection of machine learning models for a majority classification strategy that can be very effective if done correctly. The problem with creating an ensemble is the variety of different models and diversity of classification that is required to make the strategy…
