What does it take to train a Machine Learning Engineer? — Part 2
“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 before reading this post.
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
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/
AI Singapore is a national programme in Artificial Intelligence (AI) set up to enhance Singapore’s AI capabilities to power our future digital economy.
AI Singapore will bring together Singapore-based research institutions and AI start-ups and companies. These collaborations will grow the knowledge, tools and talent crucial to powering Singapore’s AI efforts.
