Introduction
With the ever-growing advancement in technology, data has become an irreplaceable part of almost every organization. As organizations strive to make sense of large volumes of data, two important roles have emerged in the tech industry: Data Engineers and Machine Learning Engineers. While both the roles revolves around working with data, their responsibilities, tools, and overall contributions to a project are quite different.
So in this blog let’s discuss the differences between Data Engineers and Machine Learning Engineers elaborately. If you are a beginner to this or still making up your mind about which career to pursue, we will walk you through with an easy explanation, realistic examples, and questions that may come across your mind. So, let’s dive in!
What Does a Data Engineer Do?
Suppose you are trying to construct a great library but to do it, you have to classify all the books, sort them, and create the conditions when every person who enters the library will be able to find the books he/she is interested in. What a Data Engineer does is quite similar but with data. They build and maintain systems that allow a seamless & efficient flow of data that can be further prepared for analysis.
Key Responsibilities of a Data Engineer:
- Data Pipeline Development: They build and maintain automated pipelines that collect, clean, and transform data from various sources
- Database Management: They maintain the integrity of the data by storing it in the most appropriate format for future access, while Cloud Engineers & Data Engineers each play distinct roles in managing and improving data infrastructure.
- Data Integration: Data Engineers ensure that data gathered from different sources are integrated and prepared correctly for other activities of the data science process.
Example:
A Data Engineer can be compared to the person who creates a mechanism that intakes raw material (data) from various source fields (data sources), processes the data, and prepares it in a usable form for the chef (data analyst or data scientist).
What Does a Machine Learning Engineer Do?
Now let's understand this with a new example. Imagine you’re baking cookies. For this you try a recipe, and after tasting the cookies, you make small changes to the ingredients or baking time to make them better with each batch. Over time, your cookies become perfect.
This is similar to what an ML Engineer does. But, here, instead of cookies, they build systems that learn and improve by identifying patterns in data. These systems can help solve problems like predicting sales, detecting fraud, or recommending products.
Key Responsibilities of a Machine Learning Engineer:
- Model Development: They create and develop models of machine learning which can also be used to predict results, for instance; sales prediction or a list of fraudulent transactions.
- Algorithm Selection: They also use the right machine learning algorithms to train their models depending on the data they obtained.
- Model Optimization: They further optimize the models so that the prediction results are the most accurate possible.
Example:
A Machine Learning Engineer is the cook who takes the ingredients that were pre-prepared by the Data Engineer and prepares a meal (model) that gets better each time it is made.
Key Differences Between Data Engineers and Machine Learning Engineers
While both roles involve working with data, the core focus of their responsibilities is what sets them apart. Let’s break it down:
A Day in the Life of a Data Engineer vs. Machine Learning Engineer
For example, imagine you have a client that sells a service and needs to identify which of the customers is likely to cancel his subscription.
The Data Engineer’s Role:
The first task that a Data Engineer does is create the data pipelines. They collect data from the company sources like, customer service, sales and support teams. This data includes interactions with customers and other transactions and even customer’s demographic details. The engineer needs to make sure that the data the organization wishes to analyze is clean, in the right format and is consistent.
The Machine Learning Engineer’s Role:
Once the role of Data Engineer ends, the Machine Learning Engineer comes in. They utilize this clean data to align a machine learning model that can make future forecasts regarding customer churn. The engineer tests multiple algorithms to evaluate the model's performance and optimize its accuracy.
In combination, those roles interact with each other. The Data Engineer guarantees the data is prepared and raw while the Machine Learning Engineer puts together a model that could make sense out of the data provided.
Data Engineer vs ML Engineer: Salaries
In the United States
- Data Engineers: They typically earn between $110,000 and $135,000 annually, depending on experience and location
- Machine Learning Engineer: Slightly higher, we are talking about $120, 000-$145, 000 per year.
In India
- Data Engineer: Earns ₹8 Lakhs to ₹12 Lakhs annually.
- Machine Learning Engineer: Higher, ranging between 10 Lakhs to 15 Lakhs per annum.
Which Career Path Is Right for You?
The decision to opt for Data Engineering or Machine Learning Engineering is a matter of preference here. If you like creating systems and manipulating data, managing databases, the data engineering roadmap could be a good occupation for you.
On the other hand, if you enjoy designing algorithms, working with the latest technologies, and with models that can make forecasts, Machine Learning Engineering may be the right job for you.
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Frequently Asked Questions
1. What is the main difference between a Data Engineer and a Data Scientist?
While both are involved in data, respectively, a Data Engineer works towards organizing and processing data, while a Data Scientist applies the processed data to create statistical models and come up with valuable findings.
2. Do I need coding skills to become a Data Engineer?
Yes, coding is essential. A Data Engineer might specialize in languages such as Python, Java, or Scala and SQL with toolkit knowledge of Apache Spark.
3. Can a Data Engineer become a Machine Learning Engineer?
Yes, most Data Engineers join the Machine Learning Engineering field by acquiring other skills such as machine learning, algorithm, deploying models, and optimizing models.
4. What tools do Machine Learning Engineers use?
ML Engineers make use of some frameworks that include TensorFlow, Keras, PyTorch, and Scikit-learn for the development of machine learning model.
5. Which career is more in demand: Data Engineer or Machine Learning Engineer?
Both jobs are popular, however, the growth of Machine Learning and data analysis has greatly contributed to the demand for Machine Learning Engineers. Nevertheless, Data Engineers are the bones of the data process, which puts them on the same level.
