ML Engineer vs Data Scientist

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In 2010, DJ Patil and Thomas Davenport famously proclaimed Data Scientist (DS) to be the “Sexiest Job of the 21st century”. The progress in data science and machine learning over the last decade has been monumental. Data science has successfully empowered global businesses and organizations with predictive intelligence and data-driven decision-making to the extent that data science is no longer considered a fringe topic. Data science is now a mainstream profession and data science professionals are in high demand a cross all kinds of organizations from big tech companies to more traditional businesses.

A decade earlier the focus of data science was more on algorithmic development and modeling to extract robust insights from data. However, as data science has evolved over the decade, it has become clearer that data science involves more than just modeling. The machine learning lifecycle, from raw data through to deployment, now relies on specialized experts including data engineers, data scientists, machine learning engineers along with product and business managers. 

The role of a machine learning engineer is gaining prominence across companies as they realized that the value of data science cannot be realized until a model is successfully deployed to production. Whilst a lot of tools and technologies such as Cloud APIs, AutoML, and a number of Python-based libraries have made the job of a data scientist easier, the MLOps of putting models into production and monitoring their performance is still quite unstructured. 

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