Data Scientist vs Machine Learning Engineer | Data Science | ML Engineer | DS vs ML | SCALER

In this exclusive video, Abhishek Vijayvargia (Senior Data Scientist, Microsoft) will walk you though the key differences between Data Scientist and Machine Learning Engineer. Join India's number one outcome-focused Data Science and Machine Learning Program now: https://bit.ly/3cWi5yh

What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

What is an ML engineer?
A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models. Machine learning engineers design and create the AI algorithms capable of learning and making predictions that define machine learning (ML). An ML engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts, data engineers and data architects. Depending on the organisation's size, they may also communicate with people outside of their teams, such as with IT, software development, and sales or web development teams.

The following topics are covered in this ML Engineer vs Data Scientist video:

00:00 - Introduction
0:26 - Comparison of Data scientists and ML engineers
2:07 - Roles and responsibilities of a ML engineer
2:50 - Roles and responsibilities of a data scientist
3:55 - Role of a ML engineer in Twitter
4:12 - Role of a data scientist in Twitter
4:43 - Salary of a ML engineer
4:57 - Salary of a data scientist
5:34 - Roadmap for a ML engineer
7:53 - Roadmap for a data scientist

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