Do you know that about 175 zettabytes of data will be produced in 2025? Data is the fuel that can make any organization or business go on a smooth journey of success and growth. This data needs to be processed, organized and securely stored by every organization for better operations and marketing strategies. With the revolutionary growth in the field of AI, the data can be organized and used in multiple ways to help businesses progress in various aspects.
Data engineering and machine learning engineering are two domains that use various computational techniques to analyze data and present the results in an insightful manner. The initial component of their work is data, but their job roles and responsibilities are very different. If you are thinking of transitioning from being a data engineer to Machine Learning engineer, we have a detailed answer for the choice.
In this article, we will cover:
Who is a Data Engineer? Data Engineering Responsibilities Data Engineering Pipeline Skills of a Data Engineer Salary of a Data Engineer Who is a Machine Learning Engineer? Machine Learning Engineer Responsibilities Machine Learning Engineering Pipeline Skills of a Machine Learning Engineer Salary of a Machine Learning Engineer Get Ready for Your Next Machine Learning Interview FAQs about Data Engineer to Machine Learning Engineer
Who is a Data Engineer? Data engineers are known as the city planners of the digital world. Their main goal is to create a system that makes data accessible so that companies can use it to assess and enhance their productivity. Data engineers construct the basic layout of the pipelines that facilitate the transmission of data through different elements of the networks.
They make sure that there is equal accessibility to the data that has been gathered from multiple sources to all other staff members working in data science, for example, data analysts and data scientists. They are also responsible for analyzing the progression and status updates of data across these platforms.
The skillset of a data engineer is important when managing large-scale processing platforms. In such platforms, scalability and performance concerns require regular maintenance. Additionally, they might create data warehouses and relational databases.
Data engineers may collaborate with the data scientists department by establishing dataset processes that help in data extraction, modeling, and execution. As a result, their contribution plays an essential role in enhanced data quality.
Data Engineering Responsibilities The key role and responsibilities of a data engineer when it comes to data engineer vs machine learning engineer are as follows:
Working collaboratively with managers to identify and meet company demands relating to storing data, administration, and analytics. Pipeline-centric data engineers typically work for medium-sized businesses, where they need to deal with more intricate data requirements. To modify the data, they need to collaborate with the data scientists and perform tasks in line with data engineering strategies. A database-centric data engineer works with pipelines and modifies them for quick data analysis and schema creation. These data engineers typically work for bigger corporations where data is spread across numerous databases. Setting up and sustaining data pipelines for gathering and transferring data from various locations to the company’s database systems. Development and maintenance of the business’s applications and hardware structures to facilitate effective and safe storage of data and administration. Performing review and debugging in order to fix any issues with the storage and management of data systems. They sift through data to identify processes where human involvement can be removed through automation. Data Engineering Pipeline Data pipelines are a set of procedures for gathering data from many sources, cleaning it, and then putting it in a database or data warehouse. The four components of a data engineering pipeline are as follows.
Data Collection: Here, information is collected from many repositories, including file systems, databases, and APIs. The data may be in structured, partially structured, or raw forms. To achieve exceptional data, diligent setup and execution are required for data collecting. Data Cleaning: Here, data is processed by looking for missing numbers, fixing mistakes, and formatting it consistently. This guarantees the accuracy and dependability of the data. Data Integration: Here, data from many sources are combined to create one dataset. This can be necessary for organizations since it ensures dependable and consistent data for efficient decision-making. Data Storage: In this step, the finished data is stored. This step is also important for quick and simplified access to data. It also plays an essential role in data security and protection. According to the project requirements, different kinds of combinations can be made using these operations. No matter where or how these operations are integrated, data pipelines always operate in a linear fashion. The pipelines always start from the original source of data and conclude with data storage.
Skills for a Data Engineer
There are several skills that one needs to have to pursue a career as a data engineer. Some of them are as follows:
Basics of data integration, management, storage, modeling and testing. Skilled in computer programming languages, such as Java, Python, R, Scala and SQL. Fundamental knowledge of building and working with a data warehouse and ELT tools. Well-versed in working with different operating systems like Linux, Solaris, Windows and UNIX. Understanding of how to use tools like HBase, MongoDB, Kafka, MapReduce and Apache Hadoop. Learn about cloud computing and AWS. Excellent presentation skills to explain to non-technical executives your project. Being proficient in machine learning, data analytics, business intelligence systems, Knowledge of how to design and develop data storage systems. Basic understanding of relational and non-relational databases. Salary of a Data Engineer The average base salary for a data engineer is $1,17,799 per year. The salary might vary according to the organization you work for.
Who is a Machine Learning Engineer? Machine learning engineers are essential players in the data science department. An ML engineer is a coding professional who creates and develops programs that can streamline AI/ML models. They are responsible for upholding and improving current artificial intelligence platforms in addition to studying, developing, and crafting the AI technology that powers machine learning.
A machine learning engineer usually collaborates closely with the data scientists who build computational models for creating AI systems and the individuals who design and operate them. They also play an essential liaison role with the remaining members of the data science department.
They create large-scale platforms that incorporate huge quantities of data and use them to formulate algorithms capable of learning cognitive functions and delivering insightful and accurate predictions. Such systems are subsequently moved to production, whereby actual users could use them - this has been referred to as the inference stage.
The complete data science pipeline is managed by machine learning engineers, covering data collection and preparation, model development and training, and model deployment to an operational setting.
Machine Learning Engineering Responsibilities When we are talking about ML engineer vs. data engineer, we need to have a clear view of the roles and responsibilities of a machine learning engineer.
Machine learning engineers are responsible for presenting ML models to their end customers. They create an extensible framework referred to as a machine learning pipeline for automating and overseeing ML operations. They are often required to create their own exclusive code with the goal of facilitating model deployment and operation within a particular setting. They collaborate extensively with front-end and back-end engineers to create programs that are powered by AI. Additionally, they interact with product managers to learn about organizational objectives and the way machine learning may help accomplish them. They should precisely record every step in machine learning procedures. The ML development process is more effectively organized with the incorporation of this kind of professional documentation. They should apply their results and insights to offer solutions about how to enhance the creation of ML services. They should use data visualization innovations to translate quantitative data into graphical representations that are easy to comprehend for non-technical executives. They need to construct a user interface that will allow users to simulate interactions with machine learning models, monitor model statistics, and gather input from experts on the subject. Machine Learning Engineering Pipeline When compared to data engineering pipelines, machine learning (ML) pipelines don't function linearly. Rather, they comprise creating, developing, and implementing ML models. Using this, every step of developing an ML model is automated, from data collection to production deployment. There are seven components in an ML pipeline.
Data Cleaning: This covers functions like separating demographics, getting rid of null data, and providing uniform date formats. Additionally, they might run experimental data analysis to find emerging trends and patterns. Feature Engineering: To enhance the efficacy of machine learning models, significant characteristics from raw data are chosen and extracted. Machine learning engineers will construct a feature archive, which is effectively a collection of versioned tables developed for each model emphasis area, such as sales, purchase, or similar areas, in order to speed up development. Model Training: It starts with identifying the best machine learning algorithm(s). After choosing the approach to use, we continue with training it using the feature-engineered data. The model structure and hyperparameter settings are typically repeated during this step until the required level of efficiency is attained. Model Evaluation: In this stage, the trained ML model's efficacy on an experimental dataset is evaluated. Once a model has been thoroughly tested and is prepared for implementation, it often goes within a model registry. Model Registry: A model registry is an essential component of a business' machine learning infrastructure. It serves as a central repository for data on the trained and used models. It keeps a record of not just the models themselves but also performance indicators for every model, along with other metadata. Model Deployment: The model will be employed and integrated into an operational framework so that it may be applied to predicting recent data. Model Monitoring: The primary monitoring methods utilized in this phase are performance evaluation monitoring and data movement monitoring. Skills for a Machine Learning Engineer To get on the path to a machine learning engineer job role, you need to know the skills required to be a well-skilled one. Here are some of the skills required are as follows:
Experience with machine learning tools like AWS, IBM Watson, Google Cloud and Microsoft Azure. Excellent command of programming languages like Python, Scala, Java, C++, R programming and C. Well-versed in applied mathematics, including statistics, probability, multiverse computations and linear algebra. Have an elaborate understanding of ML frameworks and libraries like TensorFlow, Pytorch, etc. Hands-on experience with data-related tools like Tableau, Power BI or Dash. Should be proficient in debugging and analytical skills. Have great communication and presentation skills. Have the fundamental knowledge of data modeling, evaluation and testing skills. Have a basic understanding of tools like Spark, Kafka and Hadoop. Have the flexibility to use various operating systems like Linux, UNIX and Windows. Salary of a Machine Learning Engineer The average base salary of a machine learning engineer is $1,51,151 per year. The salary might increase with experience and skill set. Also, it may vary for different organizations.
Get Ready for Your Next Machine Learning Interview Both data engineers and machine learning engineers rely on data for everyday tasks. Their duties and responsibilities, yet, are quite distinct. Machine learning engineers deploy, evaluate, and upgrade machine learning algorithms in the production setting, whereas data engineers specialize in the position of developing and handling data structures. In a data-driven company, both positions have significance because they work together with data scientists along with other staff members to generate insights and revenue from data. A transition from data engineer to ML engineer might sound a bit challenging. Interview Kickstart makes this transition journey quite smooth. We have a well-defined machine learning program that could help you crack any machine learning interview.
FAQs on Data Engineer to Machine Learning Engineer What degree do most machine learning engineers have? Most machine learning engineers have a bachelor’s degree in computer science.
Do data engineers need coding? Coding is an essential requirement for data engineers. They must be proficient in languages such as Java, Python, C, etc.
Is machine learning engineer highest paid? Machine learning engineers, AI architects, data scientists and research scientists are the highest-paid jobs.
What is the average salary of an AI engineer? The average base salary of an AI engineer is $1,41,419 per year.
Can I become a machine learning engineer from data scientist? Yes, one can easily become an ML engineer from a data scientist. Data scientists utilize machine learning algorithms and models to create insights after solving the concerns within the organization.
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