Five tips every Machine Learning Engineer must know

Dario Martinez

2021-04-22 11:37:05
Reading Time: 3 minutes

Developing successful machine learning applications requires a substantial amount of experience and state-of-the-art knowledge. Designing and implementing predictive models is often a slow “trial and error” process that gets more agile based on the expertise of the machine learning engineers involved.

In this article, I want to describe some lessons that machine learning researchers and practitioners have learned over the years, important issues to focus on, and answers to common questions. I’d like to share these lessons in this article because they are extremely useful when thinking about tackling your next machine learning problem.

1. Learning = Representation + Evaluation + Optimization

The combination of representation, evaluation and optimization is what machine learning is all about. A classifier or a regressor must be represented in formal language that a computer understands. Also, an evaluation function is needed to distinguish good classifiers from bad ones. Finally, we need a method to search among the tested models for the highest-scoring one. The choice of optimization technique is key to the efficiency of the learner and also helps determine the classifier produced if the evaluation function has more than one optimum.

2. Final Goal → Generalisation

The fundamental goal of machine learning is to generalize beyond the examples in the training set. As expected, no matter how much data we have, it is very unlikely that we will see those exact examples again in a production environment. The most common mistake among machine learning beginners is to test the training data and have a false impression of the predictive models’ capabilities. If the chosen classifier is then tested on new data, it is often no better than random guessing. Be sure to keep some of the data to yourself and test the classifier they give you on it.

3. Good model perfomance = Good Feature Engineering

4.  Representable ≠ Learnable

5. More data > Cleverer Algorithm

Where to find more useful tips…

If you found these five points useful, consider checking out a very good article from Professor Pedro Domingos of University of Washington titled “A Few Useful Things to Know about Machine Learning. If you think these tips improved your machine learning methodologies, I encourage you to take a look at the full article. And see you in my next post!

Author: Dario Martinez