A lot of people often get confused between the job roles of Data Scientists and Machine Learning Engineers. Both these professions are concerned with working with data and machine learning algorithms, so what’s the difference between them? In this article, I will take you through the difference between Data Scientist and Machine Learning Engineer.
Difference Between Data Scientist and Machine Learning Engineer
The work of data scientists and machine learning engineers depends on each other, but they are not the same. People often get confused between these two professions because these two professions are only found in large multinationals. A small organization only hires a single data scientist or machine learning engineer who is responsible for all the work from collecting the data to deploying the model. So let’s see the difference between a data scientist and a machine learning engineer.
A Data Scientist is concerned with understanding the business problem and finding a way to solve this problem by analyzing the most appropriate data that should be used to solve that business problem. They are primarily concerned with understanding the business problem and then finding ways to solve it by analyzing the data generated by the business over the years. If the problem is to be solved through machine learning algorithms rather than just data analysis, then it is a Data Scientist who needs to understand which machine learning approach should be used in this problem.
A Machine Learning Engineer is concerned with building machine learning models and deploying them to create the final product. We can say that a machine learning engineer plays the role of a software engineer in the life cycle of a machine learning model as the role of a machine learning engineer is to build and deploy machine learning models into production.
Let’s understand the difference between Data Scientists and Machine Learning Engineers using the example of Amazon Alexa. When creating a product like Amazon Alexa, the role of a Data Scientist is to decide what machine learning approaches can be used to create such a product and what types of data should be used to create such a product. Whereas the role of a machine learning engineer here is to create and train the model determined by the data scientist and then deploy that model in the cloud.
So we can say that the role of a Data Scientist is to understand the business problem and then analyze the data to solve the business problem and then deciding which algorithm can be used to train the machine learning model to solve that particular problem. Whereas the role of a machine learning engineer is to build and deploy the machine learning model as determined by a data scientist and then deploy that model to create the final product. I hope you liked this article on the difference between Data Scientists and Machine Learning Engineers. Feel free to ask your valuable questions in the comments section below.