If you wish to work with data, there is a long list of titles you can require.
The list of titles goes hand in hand with a list of technologies that you can learn.

Most current common data roles are:

• Data Engineer
• Data Scientist
• Machine Learning Engineer
• ?

But what is the difference?

Let's look at a Data Architecture and what roles required for working with large data:

Ingest & Store

"Data ingestion is the process of obtaining and importing data for immediate use or storage in a database."

Data Engineers bread and butter. Their tasks range from setting up the data platform, Ingesting the data, cleaning, normalizing, building data pipelines, and securing the data flow in the system.

Prep & Train

Prep and Train Machine Learning models are the main task for Data Scientists.
Prep and Train Machine Learning models consist of (but not limited to) articulating the problem, establishing the data collection and cleaning mechanism with the Data Engineers, and building and evaluating various machine learning models to find the best one for the business requirements.
This work can vary from descriptive analytics, predictive analytics, Deep Learning, and more. The outcome of these actions is a Machine learning model that can predict, classify, or detect necessary or unnecessary behavior.

Model & Serve

Usually, Model and Serve is the main task for ML engineers. They work with the Data Scientist team to apply model capabilities. Build an API for the model and/or embed the model capability within an application or bot. A Machine Learning model sometimes is called a mathematical model.

Web Application

Web Application is the main task of our web developers! They focus most of their time on building and maintaining the web application.

Interested in Learning more:

• Identify the tasks of a data engineer - 25 min read
• Understand the Evolving world of Data - 30 min read
• Introduction to Machine Learning with Python and notebooks - Online course
• Orchestrating machine learning with pipelines

🐦 Follow me on Twitter, happy to take your suggestions on topics.