How to go from Data Science to ML/AI Engineering

Oct 29, 2019 20 Comments

I currently have 2.5 YoE as a data scientist for a large company. Job involves running ML algorithms on data sets after cleaning/sanitizing them and working with business stakeholders to find requirements/etc. In a profit center of the company right now working on building new implementations.

I'm getting annoyed with the "70% of date science is clean-up" part and I want to do more SWE and coding. For example, the idea of building self driving cars or working on AI to launch shit into space sounds awesome.

Starting master's in computer science with ML spec next spring with Georgia tech but don't really know how to get into the space now (as opposed to latter). Also taking deeplearning.ai course on Coursera to get up to speed on Tensor flow since I just use SKLEARN now. Also only know python but gonna learn other languages soon.

Any advise appreciated! Thanks

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TOP 20 Comments
  • Cisco / Eng
    ๐Ÿ‘จ ๐Ÿป ๐Ÿ–

    Go to company page Cisco Eng

    ๐Ÿ‘จ ๐Ÿป ๐Ÿ–
    I did exactly what youโ€™re trying to do. Best thing you can do right now is automate the data cleanup and build a really good ETL engine from scratch. Thatโ€™s how I did it
    Oct 29, 2019 4
    • It's like "Do X project in Y weeks then move onto the next". I have automated things that are easily automated, but don't know how I'd automate new projects I don't know about yet.

      Although one thing to point out is that this is more about me changing careers. I want to get a new job at a new company but don't really know how to get the pre requisite experience on my resume.
      Oct 29, 2019
    • Dont forget about data instrumentation and data monitoring. Learn about concept/model drift and how to detect it. Try and automate that process
      Oct 29, 2019
  • Software engineering skills are more important now if you already do ML as a data scientist. masters in ML will be useless. The only thing that may help is strong expertise in a particular domain, like vision/robotics/nlp if you want to get job in that domain. Not that many MS programs with domain specialization, but they exist.
    Oct 29, 2019 8
    • If you do ML masters make sure you have good emphasis on fundamentals: linear algebra, statistics, optimization, distributed ML, classical ML algos, neural networks. Donโ€™t try to cover maximum number of modern application areas at once.
      Oct 29, 2019
    • And donโ€™t underestimate the importance of software engineering.
      Oct 29, 2019
  • Majority of companies hiring "data science" just end up having you scrub data and no idea what supervised vs unsupervised learning is.

    Python, etc are the new buzzwords. Interview with a few companies and they're not even far along enough with their data pipelines or data collection mediums (e.g., loyalty card) to start serious analysis.

    The other issue is there is a serious shortage of talent so B player companies can't hire anyone.
    Oct 29, 2019 0
  • I was a DS at a non-tech company that also did critical backend work and did a lot of production ops. I applied for several ML eng jobs. Got auto rejected without any interview from most except one. Ended up getting that job. Luck is huge. Shoot your shot.
    Oct 29, 2019 2
  • how about starting coding more? making more production code, huh?

    build your ML models and then deploy them end-to-end, ok?
    Oct 29, 2019 1