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Which Departments Should Play A Role in Machine Learning?

November 20, 2021

The Importance of Data Engineering in Machine Learning

When we think of machine learning (ML), data engineering may not be the first discipline that comes to mind—more likely, it’s data science. However, both data science and data engineering are integral to ML success. At a high level, data science creates methods of monetizing data for the organization (in this context by way of ML models). Data engineering, on the other hand, is a discipline of building and maintaining data-based systems. The work of data engineering ensures that data is harvested, inspected for quality, and readily accessible by appropriate data professionals throughout the organization.

Without the data that data engineering efforts provide, data scientists could not build ML models. Even further, robust ML models demand huge volumes of training data so that the models are exposed to and learn from as many scenarios as possible. Data engineering teams are responsible for ensuring that data scientists have access to the data they need—and that this data is relevant, timely, and high-quality. Data engineering is especially critical when a company seeks to move its data science project into production, standardizing the early exploratory work of data scientists into pipelines that are monitored and maintained.

How to Improve Feature Engineering for Machine Learning—Without Involving Your Data Engineering Team

Though data engineering and data science are distinct functions, there is overlap, as well. One such area where you can likely find data engineering teams and data scientists collaborating is data preparation. After data has been supplied by data engineers and before it can be used in ML models, data must be cleansed and properly formatted for the ML model. In any given organization, you may see data engineering teams or data scientists taking on components of this work.

Increasingly, as methods of preparing data become more accessible, organizations are not just turning to data engineering and data science teams, but also involving business users in the data preparation process, as well. And for good reason—not only are these resources less costly, but business users often have equal or greater domain knowledge about the data than data engineering or data science teams, which improves the feature engineering of ML models.

What is feature engineering and what does feature engineering for machine learning look like? First of all, it’s not necessarily done by data engineering teams. Feature engineering is the process of using domain knowledge to reconfigure data and create “features” that optimize machine learning algorithms. Take the example of feature engineering for machine learning on housing, provided in this article. In trying to predict the price of a house, you may be given “price” and the “latitude” and “longitude” of said house, separately. However, since the latitude and longitude do not provide the location of the house by themselves, they must be joined—thus creating a new feature. Ultimately, that simple change would help the model learn better and produce more accurate results.

Data Preparation: Where Data Engineering, Data Science, and Business Users Meet

In order to improve feature engineering (and ML models as a whole), ML is no longer being considered a singular collaboration between data engineering and data science. Having more hands on deck by involving business users in data preparation only accelerates and improves the process of feature engineering and ML as a whole. The key is providing a shared interface that everyone (data engineering, data scientists, and business users) can collaborate on—a data preparation platform.

Alteryx Designer Cloud is routinely recognized as the leader in data preparation and was specifically designed with end-users in mind. The Designer Cloud data preparation platform presents representations of data in the most compelling visual profile, and simply selecting certain elements of the profile immediately prompts intelligent transformation suggestions. It allows users to work with large, complex datasets and reduces the time spent preparing data by up to 90%. To learn how you can use Designer Cloud as part of your feature engineering process, schedule a demo of Designer Cloud today.