Ask HN: Shortest Path from Software Engineer to Machine Learning Engineer?
What are the techniques, tools and references to facilitate the above and how long does it actually take to become productive ?
- Do you want to focus on a specific area with decades of history (e.g. computer vision), or do you want to be a jack-of-all-trades person that works with images, text, time-series data, and so forth?
- Do you want to work at an early-stage startup outside of the Bay Area, which might be less picky with hiring... or do you want to work at OpenAI or Google Brain?
- Do you want to work in a team where you implement the ideas of better-educated, senior colleagues... or do you want to be the one in a leadership role?
The "minimal" path probably involves:
- A Galvanize (or similar) bootcamp to meet peers and mentors.
- Books and Coursera courses to fill in everything the bootcamp doesn't teach you.
- Winning some Kaggle competitions to show that you can build fairly complex models that have high accuracy on real-world data.
The "maximal" path probably involves:
- An undergraduate degree in Computer Science, Applied Mathematics, or Statistics.
- A PhD in one of the many subfields in machine learning.
- Several industry internships in between.
Neither the minimal or maximal path guarantee success, of course. A lot of that depends on your aptitude, previous experience, ability to network, and the current job market.
Even sharing datasets is difficult, and I recently wrote a bit about it. https://blog.helmspoint.com/posts/2017/09/05/sharing-machine...
But that doesn't even cover versioning, licensing, permanence, and provenance. The tools available for machine learning training is still nascent, so the experience isn't great.
Source: I did not know anything about how to use Machine Learning in my code just 4 months ago, and now I have just started as an AI Engineer.