ML Engineer vs. Data Scientist: The Best Choice for 2023
Data Science and Machine Learning are two concepts in the world of technology that use data to improve how we produce and innovate goods, services, infrastructure systems, and other things. Both have become in-demand and high-paying professional positions in the present world.
The relationship between the two can be understood by imagining how all squares can be called a rectangle but one cannot call every rectangle a square. Data Science is the all-encompassing rectangle, but Machine Learning is a distinct square. They are both often employed in the work of data scientists and are fast being accepted by practically every business and industry.
What is Machine Learning?
Machine Learning is a subcategory of Artificial Intelligence and the ability of machines to copy human abilities. It is used to solve difficult and complex problems in a way that humans approach complex tasks.
Computer algorithms are used to predict outcomes and results without any manual programmes. Machine Learning uses algorithms to extract data and predict trends. Models are coded into software, allowing engineers to undertake statistical analysis to understand data trends.
Some examples of Machine Learning are image and speech recognition, medical diagnosis, and traffic alerts using maps.
What is Data Science?
Data Science is a growing discipline in technology and is used to analyse large amounts of data. Using new techniques such as machine learning, artificial intelligence, and big data analytics, data scientists can process vast quantities of information quickly, find patterns within the data, and predict future outcomes. The goal of Data Science is to develop an understanding of what people do, what they believe, and how they act so that companies can design better products, solve problems, and make decisions.
Some examples of Data Science applications and examples are Gaming, transportation, healthcare services, and social media. Data Science careers offer tremendous opportunities in various industries with perks and high salaries.
Who is a Data Scientist?
Data Science professionals perform technical roles and foresee future outcomes. They have a very essential role to play and identify all the business issues and it’s a very sought-after profession in the world. Data Science professionals and scientists work with businesses and help the business to achieve tasks and goals through effective decision-making.
Certified data scientists often ask themselves how to search, identify, and analyze data, while data analysts might play an important role in guiding the team. They may also work harder at modelling, using machine learning, and incorporating advanced programming to find and sort through data.
A data scientist job profile includes working on:
- Finding data patterns and data trends in datasets to deliver insights
- Creation of algorithms and data models to predict future outcomes
- Improve the quality of data or product offerings using Machine Learning methodologies
- Data analysis using Python, R, SQL etc.
Who is a Machine Learning Engineer?
Machine Learning includes everything, including face recognition, video surveillance, and behavioural psychology. In addition, customer-facing businesses use Machine Learning to understand customers’ patterns and preferences and create direct marketing strategies.
Machine Learning engineers work as critical members of the Data Science Team. They research, build, design, maintain and improve the Artificial Intelligence (AI) system responsible for machine learning. Often, they also serve as a critical communication channel between other Data Science team members, working directly with the Data Scientists who develop the models for developing AI systems and the people constructing and running these systems.
A Machine Learning Engineer job profile consists of responsibilities like:
- Implementation of Machine Learning algorithms
- Experimenting and testing AI systems
- Design and development of Machine Learning systems
- Statistical analysis
ML Engineer vs. Data Scientist
Both, ML Engineers, and Data Scientists have separate functions in the organisation but often use the same toolset and possess the same skillset. Both are proficient in python alongside linear algebra, machine learning, statistics, and predictive modelling.
Certified data scientists are required to be more innovative in telling a story from the findings they discover. They often interact with stakeholders to understand the problem and create an optimal solution. On the other hand, ML Engineers are core software engineers. Their expertise lies in data structure design, developing efficient algorithms, distributed computing, and core computer science concepts.
ML engineers usually write code in lower-level programming languages such as C ++, Java and Scala for more efficient outcomes whereas data scientists prefer to work in higher-level languages like Python and R, and often use Power BI for data visualisation. Both are proficient in Python alongside linear algebra, Machine Learning, Statistics, and Predictive Modelling.
Conclusion
As more and more companies have become keen to take advantage of artificial intelligence trends, they are on a hunt for a well-balanced team wherein the people are well-versed in Data Science and Machine Learning. It is no surprise that data scientist and ML Engineer jobs have significantly increased and will continue to rise in the future.
