As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up -- from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering. The most pressing reason for this challenge is that the process of developing new ML models is highly uncertain at the outset.

Cultural biases in tech aren't just limited to facial recognition -- they crop up in voice assistants as well. The Washington Post has partnered with research groups on studies showing that Amazon Alexa and Google Assistant aren't as accurate understanding people with strong accents, no matter how fluent their English might be. People with Indian accents were at a relatively mild disadvantage in one study, but the overall accuracy went down by at least 2.6 percent for those with Chinese accents, and by as much as 4.2 percent for Spanish accents. The gap was particularly acute in media playback, where a Spanish accent might net a 79.9 accuracy rate versus 91.8 percent from an Eastern US accent. A second study showed how voice assistants would frequently mangle interpretations when people read news headlines out loud.