10 things I wish I’d known before starting my career in Machine Learning Engineering 💥

in #leo16 days ago

1️⃣ Figure out exactly what type of ML engineer you want to be, as the field will keep fracturing and it is important to understand what type of job you want to cover in the field ~ they can differ significantly among companies.⁣

2️⃣ Don’t overwhelm yourself with all the existing tools. The tech field is constantly evolving and new tools will be constantly coming up, you will adapt to the company’s (or your own) tools. The important aspect is to pick up new tools fast! ⁣

3️⃣ However, be a solid software engineer! ⁣

4️⃣ You don’t know what type of data you will be working on. It is important to have Inter-disciplinary skills. Take time in college to learn about other subjects apart from statistics and computer science, they will be very helpful in your career in the future.⁣

5️⃣ Be active working in projects with other enthusiasts of the field, this will help you broaden your knowledge and get some real life experience. You can do this by participating in Hackathons, Conferences or simply code sharing on GitHub! ⁣

6️⃣ Never try to memorize algorithms. The important aspect is to understand how to research SOTA methods and implement them “on the way” ~ you can’t achieve this just by memorizing formulas! ⁣

7️⃣ Work on your communication skills, which we sometimes forget to do when studying an engineering! You will need to understand business cases and clients, as well as communicate results. ⁣

8️⃣ Keep experimenting with different fields where you can apply your ML knowledge. Research on existing products that you can improve or change, this will help you expand your creativity. Innovative solutions are critical in ML. ⁣

9️⃣ The most important skill is critical thinking! You need to understand what it is important and what’s not when developing data products. ⁣

🔟 The learning process can be slow, as the field is big and evolving. Never stop learning I you will eventually start feeling more confident in the field! ⁣

Am I missing something? What do you think?


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Interesting stuff, might be useful for people following ML path.

Thanks! hopefully!