“Developers, developers, we need more developers,” comes the chorus. But what does it take to train an engineer, especially an engineer familiar with Machine Learning?
This is the second post in a series looking at pain points we’ve seen as our newbies start moving into the ML space. If you have not read Part 1, click here.
Understanding what an end-to-end pipeline looks like
“Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing.” Jeremy Howard, Designing Great Data Products
Spot where ML code turns up!
There are good thought-pieces on how to build Machine Learning’s version of the Full-Stack Engineer here and here. I must say taking this end-to-end approach is both good and bad (as discussed in this awesome podcast).
Personally, I like how this forces me to focus on the simple. If I have to manage a data-preprocessing pipeline, a model and an API, I need to make sure each component is easy-to-understand. My head can only take so much complexity from so many areas before it starts to hurt.
For more information on what we do and our AI Apprenticeship Programme, visit: https://aisingapore.org.aiap/