Hi All,
I broadly see 2 different Job opportunities and also relevant topics such as Data scientist and ML Engineer…
Can anyone elaborate where one stops and one begin? basically the boundaries between these two…
Best Regards,
Mohan
Hi R_Mohan_Kumar! Thank you for the question.
This is really a tricky question because nowadays things are more and more related.
Data Scientist
In general, we say that the Data Scientist are responsible for analyzing and understanding specific business problems, feature engineering, developing, selecting, and (sometimes) tuning models. In this sense, the data scientist is responsible for glean insights from the data.
ML Engineer
Machine learning engineers come into play after the model has been built by the ds. A machine learning engineer will focus on writing code and deploying machine learning products.
As it is said in this post: “Of course, machine learning engineer vs data scientist is only the beginning of nuances that exist within relatively new data-driven disciplines.” These definitions and roles performed by each may vary from company to company as well. Therefore, it is important to always check what the company requires in terms of knowledge and requirements for a particular job.
Here are some links that can help you:
- MACHINE LEARNING ENGINEER VS. DATA SCIENTIST
- ML Engineer vs. Data Scientist: What’s the Difference?
- Data Scientist vs Machine Learning Engineer Skills. Here’s the Difference.
Any questions don’t hesitate to ask,
Bests,
Wesley P.
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What about Machine Learning Engineer vs MLOps Engineer? Are they the same role?
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MLOps Engineers mainly deal with the operational and practical side of things whereas Traditional ML Engineers often deal with research and development genre
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