Prompt: Hello, I study to become ML Engineer. As someone with your experience, what would be your advise to someone like me who wants to be in that field but doesn't have the background in it?
Machine Learning is a very broad and deep field to study, which can take many years to learn. However I would advice you to start small. Given some data, can you find some insights from this data that are interesting, actionable, or simply support a story that you want to narrate? You can just use pen and paper, or Excel sheets to start with. I would suggest to use tools readily available online, and build working prototype -- a prototype that is maybe ugly but solves the problem quickly. Then keep digging deeper into how it works underneath.
Further, can you create a system, that periodically finds such insights, from such data that is updated over time? Interestingly you don't necessarily have to always build complex ML models to help end-users.
Then can you create a portfolio of few projects highlighting how these projects would help end-users? In majority of the cases, the complexity lies in building an end-to-end solution, of which ML is a very small part. For long-term success, most important concept is to understand how your solution will help the end-user. In the end, if your solutions ( also ML-models) don't help save time / money / effort, they will die off sooner than you would expect. Success in this regard means you understand how the business works, and what the end-users would really want -- not necessarily that users know what they want, as sometimes users don't know what they want.
If you want to become a machine learning engineer without a background in the field, here are some tips based on the search results:
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Start with the Basics: Begin by learning the fundamental concepts of machine learning, such as linear algebra, calculus, and probability. Practical coding experience is also crucial. Consider working on simple projects and gradually deepening your understanding of machine learning concepts
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Stay Up to Date: Machine learning engineering is not an entry-level job. It requires experience in data science and data engineering. Keep yourself updated with the latest developments in the field
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Choose a Niche: Pick a niche in machine learning and follow along with tutorials. Consider starting with a simple project and gradually expanding your knowledge from there. It's also important to show your passion for applying machine learning and to demonstrate good communication skills
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Develop Technical Skills: Familiarize yourself with machine learning frameworks such as PyTorch, TensorFlow, Scikit Learn, and XGBoost. Additionally, focus on developing soft skills such as problem-solving, business acumen, and the ability to adapt to changing requirements
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Master the Basics: Spend time mastering the basics of machine learning, including data evaluation, ETL tools, database management systems, and SQL programming language. Consider working on beginner-friendly projects and continue learning even after securing your first machine learning engineering position
By following these tips and continuously learning and practicing, you can work towards becoming a successful machine learning engineer.