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The data world can be confusing to say the least and it’s ever changing! Want to learn more about what’s involved in managing a data lifecycle? What to consider? What’s the difference between a data scientist and a machine learning engineer? What does MLOPs even mean? We can help. We’ve written some handy guides and filmed some video explainers on your common questions. If you’re a reader we’ve got detailed content for you or if you just want a quick overview you can watch a video. If you’ve an idea for something you want explained in clearer terms, we’d love to hear from you. Get in touch below.
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What are the differences between a Data Scientist and Machine Learning Engineer
A data scientist’s key responsibility is to understand business problems and develop one or more machine learning algorithms to solve them. Then, the specification of the ML models is handed over to ML engineers to be optimised and deployed to a large scale. In terms of skill set, both roles share many similarities such as communicating with confidence, version control and being proficient with cloud services. Conversely, both roles require vastly different domain knowledge; data scientists should be strong in statistics and business acumen, whereas ML engineers should be proficient in fundamental software engineering skills.
September 13 2022