Data Scientist Vs Data Engineer Vs ML Engineer Vs MLOps Engineer

The previous years have seen dramatic advancements in data science and machine learning. Data science, machine learning, and data engineering are the three most sought-after data-related occupations. But what precisely do these positions entail?

Because the professions of Data Scientist, Data Engineer, ML Engineer, and MLOps Engineer are still developing, there is some misunderstanding regarding the differences between them. Are they essentially alike each other? But if you break things down and look at the semantics, the differences become clear.

On a broad scale, we're referring to engineers and scientists. An engineer's job is to make things, whereas a scientist is required to fully comprehend the science that underlies their work. Let’s get going and try to differentiate between these fascinating job roles that are more often been confused with one another.

Data Scientist

Defining Data Science:

Data is at the center. Streaming into company data warehouses are vast amounts of unprocessed data. It may be mined for many lessons and sophisticated capabilities can be created using it. Data science is essentially about finding new methods to use this data to benefit businesses.

In an easier way, we can define data science as the application of statistics, mathematics, programming, and subject knowledge to the analysis and extraction of valuable data-driven insights. It is an interdisciplinary subject of scientific procedures, procedures, algorithms, and systems.

Who is a data scientist?

Data scientists, one of the important positions in the field of data science, employ learning to glean information and insights from both organized and unstructured data. Additionally, the discipline enables experts to apply their understanding and use data insights across a wide number of application fields.

A data scientist is a scientist who focuses on solving business problems by researching, identifying, and analyzing them. Machine learning techniques are typically used to accomplish this. A data scientist will typically start by reviewing the information and offerings from their own firm. They will then talk about the company’s problems and develop solutions while collaborating with the data engineering team. They will then choose the best model to employ for the functionality of the business, eliminate duplication of any sort, and present the organization with an effective solution.

What do Data Scientists do?

• Deciding on features, Machine Learning Techniques for Classifier Construction, and Improvement.
• Recognizing the needs of the client's business and directing them to a solution.
• Using modern techniques for data mining.
• Carrying out market research.
• Collecting Information and Assessing the Strength.
• Use MXNet, Tensorflow, Theano, and Keras as Deep Learning frameworks to create Deep Learning models.
• Precisely find Trends, Correlations, and Patterns in Complex Data Sets.
• Cleaning, preparing, and ensuring the accuracy of data used for analysis.
• Finding fresh opportunities for process enhancement.
• Engaging in Business Services DevOps specialists can assist clients in operationalizing models after they are constructed.

Why being a data scientist is worth it?

The demand for data science on the market has been continuously increasing, and it is the newest buzzword in the IT industry. Due to enterprises' increasing need to turn data into insights, there is an increasing demand for data scientists. Among the top employers of data, scientists are businesses like Microsoft, Google, Amazon, and Apple. For IT professionals, data science is also growing in popularity.

Data science has given positive predictive intelligence and data-driven decision-making to international enterprises and organizations to the point that it is no longer regarded as a fringe topic. A new generation of analytical data professionals, the data scientist is a vital actor in enterprises today.

The big data industry is ruled by this group of people who are a mix of mathematicians and computer scientists. Businesses today are struggling to make sense of vast amounts of unstructured data that, when uncovered, maybe a virtual gold mine for increased revenue. But businesses truly need experts who can delve in and unearth priceless business insights, sorting through the pointless chaff and uncovering the priceless pearls of information. The data scientist does that, which explains why they are highly sought-after and well-paid.

By all accounts, a career as a data scientist is very appealing. According to the median base wage, the amount of vacant positions, and employee satisfaction levels, Glassdoor has consistently placed data scientists as one of the top 10 careers in America. Similar to how Harvard Business Review described data science as "the sexiest job of the 21st century," it noted that there is a strong need for "high-ranking experts with the training and passion to make breakthroughs in the world of big data." Data scientists are in high demand throughout all types of organizations, from large tech companies to more established companies, as data science is increasingly a mainline career.

As per Glassdoor.com;

• A data scientist’s salary in India on average is 10,00,000 LPA.
• A data scientist in US receives an annual salary of $121,263.
• The salary for a data scientist in UK is £55,242 yearly.

Data Engineer

Defining Data Engineering

Data aids organizational development and is crucial since it makes it easier to make informed decisions. But organizations need the right people’s help to find and collect the relevant data - the data engineers role comes into play right here. SQL's introduction in the 1970s gave data engineering a jumpstart, and later on, NoSQL's mainstreaming gave it a further push.

Data engineering is the act of creating and constructing systems that enable users to gather and evaluate unprocessed data from a variety of sources and forms. These technologies enable users to discover useful business uses for the data, enabling enterprises to prosper.

Who are Data Engineers?

The framework on which data scientists and machine learning engineers operate is set up by data engineers. They are in charge of storing and moving data at the proper volume and speed for the intended use. Data engineers are essentially software engineers who focus on data pipelines and making sure that data flows where, when, and how it is required for these models to really perform.

Before presenting raw data to an organization, a data engineer’s primary goal is to transform information into something usable and comprehensible. In addition, they must use data from various sources to design, construct, test, mix, manage, and optimize it. They produce the infrastructure needed to produce this data. Their goal is to construct efficient data pipelines. In addition to all of this, they create difficult queries to guarantee that the data is accessible.

What do data engineers do?

• Create and maintain optimal data pipeline architecture.
• Create the infrastructure needed for efficient data loading, transformation, and extraction.
• Generate new tools to aid data engineers and analysts in the development and optimization of products.
• Understanding the organization's goals should be done in collaboration with the management.
• Building an optimum system in data delivery that ensures greater scalability.
• Constructing, assessing, controlling, and keeping the database

• Converting raw data to accessible and useful data through the development of algorithms
• Find datasets that meet the needs of the business by sourcing them.
• Create novel validation techniques and data analysis tools.

Is a career as a data engineer worth it?

Technology has seen the most surge in data engineering out of all the sectors. A 2020 Dice Tech Job Report predicts a 50% increase in open positions year over year, which indicates that the expansion of data engineering has quickened in the technology sector.

Data engineers are ubiquitous in the services, marketing, and banking sectors and indeed in computer software and retail. The need for data engineers has multiplied five times compared to the need for data itself. According to reports, Data engineers will be more in demand on average between 2017 and 2025, with a projected increase of 31% annually.

Given the increased demand for data engineers, it appears like a tempting career choice for anyone considering a change. Because of the widespread reliance on data, this rise in demand might keep growing. The subject of a data engineer's salary naturally arises while discussing the specifics of this position. Obviously, a person's educational background, skill set, and experience will all affect the wage range.

As per Glassdoor.com;

• A Data Engineer’s salary in India on average is 8,00,000 LPA.
• A Data Engineer in US receives an annual salary of $114,557.
• The salary for a Data Engineer in UK is £52,766 yearly.

Machine Learning Engineer

Defining Machine Learning Engineering

The accessibility of the massive-scale machine learning infrastructures led to the emergence of the field of machine learning engineering in the last five to seven years.

The deployment and management of machine learning models in production are done through the art and science of machine learning engineering (MLE).

Or we could say that machine learning engineering is the process of employing software engineering concepts, analytics, and data science skills in order to take an existing machine learning model and make it usable by the end product/consumer.

Who is a machine learning engineer?

A machine learning engineer is essentially a software engineer with ML knowledge who transforms the statistical or machine learning models created by data scientists into a live production system. Programs for controlling computers and robotics are also created by machine learning engineers. A machine may learn to recognize patterns in its own programming data, understand commands, and even think for itself thanks to the algorithms created by machine learning specialists.

Prospective ML engineers must be knowledgeable with machine learning techniques, have experience with software engineering and a range of programming languages, as well as a solid background in mathematics and data analysis. It is essential for them to have the mastery of specialized technical tools for deploying machine learning systems, managing data pipelines, and monitoring and debugging them.

What do ML engineers do?

• Construct ML Models
• Develop data and model pipelines collaboratively with data engineers.
• Design distributed systems using machine learning/ data science methodologies.
• Produce code for production.
• Organize entire lifespan - (research, design, experiment, develop, deploy, monitor, and maintain.
• Put machine learning algorithms and libraries into practice.

• Inform business leaders of complicated procedures.
• Gain important insights, and analyze huge, complex data sets.
• Develop current machine learning models.
• Conduct studies and put best practices into effect.

Is a career as an ML Engineer worth it?

Machine learning engineering is a relatively young field of study, but it is growing in popularity, and engineers are in great demand. With the almost infinite possibilities offered by this technology, employment opportunities are likely to exist across a wide range of industries.

Despite being a relatively young field of work, machine learning engineering is flourishing and in great demand for engineers. This technology offers practically endless potential, which opens up a wide range of work prospects. According to Fortune Business Insights, the global market for machine learning is forecasted to reach $209.91 billion by 2029 compared to $21.17 billion in 2022! Consecutively, the need for data specialists in the job market has never been higher as a result of the huge interest in big data among many businesses.

Most machine learning engineers hold a Master's degree in computer science or a closely related field of data engineering education. But that education is merely a starting point; it cannot ensure career success. The ability to work with huge, complicated datasets, excellent analytical abilities, high attention to detail, communication abilities, and originality and creativity are sought after by ML Engineers.

The top job of 2019 according to Indeed is machine learning engineer, and for good reason. As per Glassdoor.com;

• A Machine Learning Engineer’s salary in India on average is 9,00,000 LPA.
• A Machine Learning Engineer in US receives an annual salary of $136,390.
• The salary for a Machine Learning Engineer in UK is £54,672 yearly.

MLOps Engineer

Defining MLOps

From Data Engineering to Data Science to Machine Learning Engineering, it also requires cross-team communication and hand-offs. It goes without saying that a high level of operational discipline is required to keep all of these processes coordinated and operating simultaneously. The experimentation, iteration, and continuous improvement phases of the machine learning lifecycle are referred to as MLOps.

One of the most fascinating branches of AI is machine learning, but because it is so diverse, there is a need for specialization. MLOps is one such area of expertise that is revolutionizing the AI sector. The field of Machine Learning Operations, often known as MLOps, combines machine learning, data engineering, and DevOps.

Who is an MLOps Engineer?

The operations and management of machine learning models, algorithms, and procedures are the primary areas of interest for MLOps engineers, who are developers. They collaborate with data scientists to ensure that their projects are utilized properly, and they keep an eye on the well-being of the models they develop. MLOps engineers must possess interdisciplinary knowledge in the fields of operations, data engineering, and data science.

What do MLOps Engineers do?

• Tasked with evaluating models and adjusting hyperparameters in them. Onboarding, operations, and decommissioning workflows are all modeled by these engineers. version governance and control models, data preservation, and version management the model's evolution under observation.
• To assess and improve the quality of the services, develop and use standards, metrics, and monitoring.
• Delivers the finest alternatives and carries out proofs-of-concept for automated and effective model operations on a broad scale.
• Builds and keeps up scalable MLOps frameworks to support model-specific client needs.

Is a career as an MLOps Engineer worth it?

The need for professions relating to data has skyrocketed during the last few years. Job openings connected to machine learning and artificial intelligence have increased by 74% annually. People from many walks of life are attempting to enter the data sector. Data science is typically the area of concentration for anyone trying to enter the data industry. Change your attention to MLOps since it is a similarly high-paying industry that is still relatively untapped.

As more businesses begin to understand that data scientists alone are unable to fully utilize the potential of machine learning models, the MLOps industry is expanding quickly. No matter how good a machine learning model is, it is useless if it cannot be used. With tools and methods that are certain to keep changing swiftly, this is a brand-new and fascinating discipline.

MLOps is expanding quickly, and by 2025, it's expected that the market for related products will reach $4 billion. Due to the current lack of workers with the combined skill set of data scientists and DevOps engineers, there are also a lot of employment opportunities in the MLOps space. There is undoubtedly a lot of room to grow and apply production strategies to ML. They receive a decent salary, too.

As per Glassdoor.com;

• An MLOps Engineer’s salary in India on average is 11,61,078 LPA.
• An MLOps Engineer in US receives an annual salary of $94,515.
• The salary for an MLOps Engineer in UK is £37,863 yearly.

Final Say

Are you one of those people who read with glee about the most recent developments in artificial intelligence or computer applications because they are merely captivated by technology? You should know that there is ample space for career growth and development be it in data science, machine learning, data engineering, and MLOps and they all provide lucrative career routes. Imagine passing a baton off as one approach to analyzing the connection between these domains that are essentially interconnected.

We do, however, think that the blog has in some way been beneficial to you. In case you are interested in pursuing a career in any of the above domains, a certification course is a right way to begin. DataMites is a global training provider with accreditation from IABAC that offers specialized courses in machine learning, data science, MLOps, data engineering, python, and even more with a meticulously sketched-out syllabus that is designed to impart extensive knowledge from the very basics to the advanced level.