• A data engineer can become a machine learning engineer with proper training. Keep in mind that being a data engineer is not a prerequisite for becoming a machine learning engineer.

    I find this question very similar to Can data engineer become a data scientist?. Do take a look at that discussion as well.

    Data engineering is an emerging field in the big-data domain and there's a growing demand for data engineers.

    A typical data engineer role involves collecting data, creating data pipelines, verifying the data, correcting it and making it available for further consumption. The role requires dealing with cloud tools provided by AWS, Google and Microsoft Azure. There are programming languages for data engineers that help solve data handling problems.

    A machine learning aka ML engineer on the other hand is required to implement machine learning algorithms. There are several libraries and tools that ML engineers use. More specifically, machine learning engineers need to have an in-depth knowledge of computer programming. Google Tensorflow is an important library to make a start as a machine learning engineer.

    Therefore, a data engineer can become a machine learning engineer; but it's not a prerequisite. Let me know if anyone has questions about this topic.

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  • Steve Gracia


    A data engineer can definitely become a machine learning engineer. It's all about leveraging your existing skills as a data engineer and acquiring new skills required for machine learning.

    Here's a step by step approach to transitioning from data engineer to a machine learning engineer-

    Start with the basics of machine learning:

    If you're not already familiar, begin with understanding the basic concepts and algorithms of machine learning.

    There are excellent online courses that offer a great start. For example, Coursera's "Machine Learning" by Andrew Ng is a classic that covers a wide range of foundational topics.

    Deepen your programming skills:

    As a data engineer, you're likely already proficient in programming languages like Python.

    This is great because Python is also a go-to language for machine learning. To sharpen your skills further, focus on libraries such as NumPy, Pandas for data manipulation, and then move on to Scikit-learn for machine learning, and TensorFlow or PyTorch for deep learning.

    Work on projects:

    Hands-on experience is invaluable.

    Start with small projects that interest you, maybe something related to data you've worked with as a data engineer.

    Websites like Kaggle not only provide datasets but also competitions that can help you apply what you've learned in a practical setting.

    Understand the data pipeline:

    As a data engineer, you have a head start here. Machine learning engineers need to know how to work with data pipelines too, but with a focus on how to feed data into models effectively.

    Improving your skills in data preprocessing and feature engineering can be very beneficial.

    Learn about machine learning deployment:

    Understanding how to deploy machine learning models into production is key. This includes knowledge of cloud services like AWS, Google Cloud, or Azure, which you might already be familiar with, but now with a focus on services like AWS SageMaker, Google AI Platform, etc.

    Continuous Learning:

    The field of machine learning evolves rapidly. Keep yourself updated by following relevant blogs, attending workshops or webinars, and participating in community discussions.

    I hope you find this useful. Let me know if you have follow-up questions. I'll be happy to help.

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