Data Scientist Vs ML Engineer: Key Differences |Data Science |Machine Learning | #Shorts Intellipaat
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Check out what Mr. Srinath Sivalenka, Ex Microsoft, has to say about the Key Differences between a Data Scientist and an ML Engineer.
He says that a Data Scientist is good at building modules and understands
Machine Learning on the other hand ML Engineer is responsible for making the code more production-ready.
So, whether you're someone who knows a lot about artificial intelligence or you're just beginning to learn about it, this Podcast will give you a guide on how AI is going to shape the future of work.
✅ Can an ML engineer become a data scientist?
Yes, absolutely. A data scientist is essentially a person who uses machine learning to build models and algorithms for solving problems, which then turn into insights that companies and other organizations can use.
✅ Which is better ML engineer or data engineer?
Machine Learning Engineers are more specialized backend developer roles. Ideally, they need some software engineering experience first. No degree is required, but some data engineers who work with difficult and novel computer engineering problems may actually need a computer science background.
✅ Is ML harder than data science?
The consensus is that data science is in fact easier than machine learning. Data science involves more statistics, while machine learning involves more computer science in addition to statistics.
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