Do Data Engineers Do Machine Learning

Do Data Engineers do Machine Learning as well? Let’s find out. 

Data Engineering is the gush of the hour. Tons of companies are in search of qualified Data Engineers that can derive value for their business. Being a competent Data Engineer is no joke. It requires you to develop many skills, and Machine Learning is indeed one of them. 

Keep reading, and we’ll know up to what extent do data engineers do Machine Learning.

What is Machine Learning?

Machine Learning, Data Science, Artificial Intelligence are the hot topics of the decade and the buzz words you get to hear often. 

But WHAT is Machine Learning?

Is it like a school of Machines, where someone teaches all the machines to perform a particular task? 

Not really. 

And trust me when I say this, these technologies are here to stay for a long time. So let us get acquainted with Machine Learning. 

Machine Learning is a subset of AI, and it deals with data. 

We have a lot of Data around us, don’t we? 

However, without Machine Learning, all the data we have is pretty useless. Machine Learning strives to derive meaning from the datasets.

Machine Learning enables a system to learn from the data without being explicitly programmed.  

The above statement might pose the following question in your mind: How will the machines learn if they aren’t programmed? Is ML magic?

Nope. Machine Learning uses algorithms to understand the data, predict a future outcome, and improve its accuracy. 

That’s it. ML is a task to make our computers more intelligent without actually teaching them. 

Data Engineering VS Machine Learning

The working of these two domains, in a nutshell, is as follows:

While Machine Learning is more like a backend task, it deals with implementing algorithms, deploying the models, and ensuring the end goal and output accuracy meet the specified standards. 

On the other hand, Data Engineering deals with designing, building, and maintaining the data infrastructure that aligns with the needs of the business.  

Do Data Engineers use Machine Learning?

Do Data Engineers do Machine Learning? Do they perform the same roles?

Data Engineers are Software Engineers that service data. Their job is building data pipelines, ensuring data flow, cleaning the data, designing, developing, and maintaining the architecture to store the data. 

So, do data engineers need to know Machine Learning?

Data Engineers don’t need to know the depths of Machine Learning. 

The basics of Machine Learning would prove beneficial as they will help a Data Engineer understand the organization’s requirements and enable them to build accurate ETL pipelines and infrastructure.

Data Engineers should have a good grip over the following concepts:

1. Basic Machine Learning terminologies and algorithms.
2. Gain technical knowledge on how to embed the Machine Learning model into the system.
3. Understand how to develop Machine Learning Pipelines or MLOps.
4. Knowledge on how to scale Machine Learning operations based on business needs.

Machine Learning for Data Engineers

To gain or sharpen your ML skills, you may find many online or in-person classes in your locality. The first step is always very crucial and overwhelming at the same time. 

Thus, we recommend that you learn the necessary Machine Learning skills by following the KCE process. It is a straightforward and highly effective procedure, irrespective of the mode of learning you choose.

KCE is the acronym for Knowledge, Certification, and Expertise. 

It aims at helping you master any skill by helping you lay a strong foundation and then validating it through practical exposure. 

In the first step, knowledge, you need to understand the real-life applications of both Machine Learning and Data Engineering. 

This step will help you understand the roles of these engineers, identify the skillset for each, and decipher the overlapping skills. 

Moving forward, now you have an idea of the overlapping skills. To learn the basics of Machine Learning, you need to take different courses.

The best way to verify your credentials is through certifications. There are numerous resources (free and paid) online that can help you achieve this goal.

Be consistent, learn from the courses and earn a certification. This practice will help in building a stellar portfolio along the way. 

You have the skills, but they aren’t consistent with real-world applications unless you apply them. 

There’s a gap between theoretical knowledge and actual use of the concept. To bridge it, you need to undertake internships and projects.

This step will help you develop analytical skills to apply what you’ve learned to solve the actual problems.

While many companies look for a Data Engineer with intermediate Machine Learning skills, others prefer having a specialist handling each domain separately.

There are various modes available in the market to learn and develop a skill. You can learn online, in-class, or even take blended courses.

However, we recommend that you choose in-person classes whenever possible as they are lively, interactive, and offer a good learning environment.

So, do Data Engineers do Machine Learning? The answer is: yes, they do up to some extent.  

As a Data Engineer, knowing the basics of Machine Learning will help you in the long run. Most Data science projects never make their way to production as they aren’t clear, or Data Engineers lack the relevant skills.

Thus, build up your skills, follow the KCE method and polish your profile to become the best Data Engineer in the market!