# Preparing your product for machine learning

DevFeed: [Preparing your product for machine learning](<https://devfeed.tech/articles/preparing-your-product-for-machine-learning-32026.md>)

Original publisher: [Read original article](<https://tech.finn.no2018/08/09/preparing-your-product-for-ml/>)

Author: Joakim Rishaug

Published: 2018-08-09T08:00:00Z

Content type: tutorial

Language: en

Sources: [Finn.no](<https://devfeed.tech/sources/finn-no.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>), [Kaggle](<https://devfeed.tech/topics/kaggle.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [introduction](<https://devfeed.tech/tags/introduction.md>), [kaggle](<https://devfeed.tech/tags/kaggle.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>)

## AI overview

This article explains how engineering teams can prepare products for future machine learning work, even when data scientists are not yet available. It emphasizes engineering fundamentals, useful features, numerical data, and reducing problems caused by unstructured or incomplete data.

## Source excerpt

At many companies, there are few data scientists and many projects which may be very interesting for the business to apply machine learning on. For most of these companies, starting greenfield projects with a data scientist on the team might be either a difficult political battle (to get assigned resources) or impossible, if there are no data scientists yet. But how can a team prepare their project for the day when there will be a data scientist around, and in the process, maybe improve their current product as well? In Google's "Rules of ML", they make some good suggestions for this in their introduction: Do machine learning like the great engineer you are, not the great machine learning expert you aren't. Most of the problems you will face, are in fact engineering problems. Most of the gains come from great features, not great machine learning algorithms. This means, the team already has great potential to improve their product in ways that the data scientist might help them do in a more structured way later. I believe as long as the engineers and programmers know how machine learning algorithms like having the data shaped, they can take advantage of this to move forward confidently. What kind of data does a machine learning algorithm like? Computers like things that are quantifiable by numbers, and this is probably obvious to many programmers, but it's easy to forget this when we have tools like Google that can find and seemingly understand what we want just by typing a couple of words. According to a Kaggle survey from 2017, "dirty data" is the biggest problem faced by machine learning practitioners. This means data being unstructured in some form, either by being severely skewed in one direction or the other, being full of holes (only subsets of users even have a given feature), or data that has to go through processing before actually becoming a usable feature. Making sure that this friction is minimized is important later for the progress of machine learning