Machine Learning Engineering is really trending heavily in the international job market. It is all set to take over the sexiest job title from its contemporary peers, the data scientists, even as ML engineering is known to attract thousands of professionals into high profile data analytics courses. Many industry experts feel that the data scientists are actually facing stiff competition from the new generation of Machine Learning engineers who say that ML is more about innovating with data science rather than just taking the business route to knowledge management and so on.
Let’s understand the nature of competition between the elite groups of data science courses in this article.
Data Science Definition has Evolved Dramatically between 2015 and 2021
It’s true that data science would always find its roots in how data is procured, analyzed, re-engineered, and applied to different tasks and activities. What started as a means to improve and create new styles of business intelligence for analytics groups in an organization has now become the ubiquitous deck for all functions within the same organization. Every group – Marketing, Sales, Finance, HR, and even IT and Security departments are looking at data from a more professional angle, enabling these respective teams with powerful actionable insights to improve operational efficiency and save costs of process management. Data science in the last 3-4 years has acquired the legacy of turning any department into a data-driven setup that brings together the art and science of creating new models of managing business analytics using Mathematics, Statistics, market research, and financial management. Today, it’s impossible to think of a company that doesn’t rely on data scientists for their business analytics purposes.
And, that’s where the Machine Learning engineering groups are hitting hard on the data scientists.
ML Engineers are taking the shortest, but the smartest routes possible to create cutting edge models for analyzing and monitoring data for advanced business intelligence, such as Automated Cloud Security management or AI-based customer service management. With automated coding built on Open Source Low-code platforms, ML engineers are able to turn any business group into a data science focused users, enabling these users with really cool self-service programmable decks that can do quick validation and quality checks on types of data used to build ML models for their respective tasks.
I will give a straightforward example that you can relate to in the ML domain.
Companies like Google and Facebook (now called Meta) are hiring ML engineers to amplify the reach and depth of their respective AI programs within the general IT group of users who are convinced that the world is in more need of AI ML engineers than data scientists. Reason: ML engineers can develop the same roadmaps with data as data scientists using much lesser resources and they come cheap.
If we closely look at the recent trends, we will find that demand for data scientists might fall to ML engineers for advanced work.