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He lost me here. Something I've always loved about being an engineer (and now in product) is that something small we do/tweak can have big impact.
If you tuned a parameter and that actually had tangible impact on the business, that's like the best case scenario and should be celebrated (vs doing some cool rocket science stuff that ends up unused and doesn't matter)
This is true everywhere. As a professor, every semester I’m baffled by students who aren’t curious. But I’ve come to terms that there is a difference between those who will graduate and go on to be readers of hacker news and write this kind of article, and those who won’t.
Earlier quoted context omitted.
would you highlight some of the biggest differences between ML engineering and data engineering? I believe they're sometimes used interchangebly especially if "data" is "datasets" for ML.
Data engineers don't work with machine learning at all. In fact one of the reasons why it developed as a job title over time waas specifically to differentiate the people who work with data but don't do any statistics or ML. If a DE who is doing "datasets for ML" decides to call themselves an ML engineer, they're just getting a bit too creative with the job titles (maybe they want a career change, more money, they th…
This very much depend on the company. From experience DE is used as a catch-all title.
"Managers will say they want to make data-driven decisions, but they really want decision-driven data" Ooofff. This is too true. How often is the case that data is collected to test hypotheses vs confirming priors?
Find me some evidence of WMDs in Iraq! Yessss Sir!
"Managers will say they want to make data-driven decisions, but they really want decision-driven data" Ooofff. This is too true. How often is the case that data is collected to test hypotheses vs confirming priors?
Earlier quoted context omitted.
Data engineers don't work with machine learning at all. In fact one of the reasons why it developed as a job title over time waas specifically to differentiate the people who work with data but don't do any statistics or ML. If a DE who is doing "datasets for ML" decides to call themselves an ML engineer, they're just getting a bit too creative with the job titles (maybe they want a career change, more money, they th…
> Data engineers don't work with machine learning at all. This very much depend on the company. From experience DE is used as a catch-all title.
As someone who also wants to move away from data science, data engineering is the last thing I would want to do. I think DE comes with many of the same problems and it's also a very ill-defined career track; I wouldn't recommend it to anyone. ML engineer or backend developer seem like much more appealing job profiles.
would you highlight some of the biggest differences between ML engineering and data engineering? I believe they're sometimes used interchangebly especially if "data" is "datasets" for ML.
Data Engineers are the people who take raw data (e.g. what lands in S3) and put that into data systems that can be used by other systems (e.g. Dashboards) and people (e.g. Analyst, Data Scientists, BI people). Data Engineers clean data, but they are really looking at cleaning out systemic issues (e.g. some data that is missing in one field is in another field, and that needs to be consolidated) and not the scrutinized row-by-row cleaning that Data Scientists end up doing. Data Engineers also do the data steps (e.g. creating a performant stored query) required to support things like business KPIs and reporting.
ML Engineering has a lot more variety based on the company and org, but generally it's about building an automated pipeline that includes ML. In smaller orgs you do everything - build a data pipeline, train a model, deploy that model, score new data, etc. In larger orgs, ML Engineers take a model built by somebody else and make it run at scale while meeting certain SLAs (e.g. making recommendations on a social media website).
> But there’s also a part of me that’s just like, how can you not be curious? How can you write Python for 5 years of your life and never look at a bit of source code and try to understand how it works, why it was designed a certain way, and why a particular file in the repo is there? How can you fit a dozen regressions and not try to understand where those coefficients come from and the linear algebra behind it? I d…
I have more important things to do. The hacker mentality, imo, is about identifying what’s useful for you to explore to accomplish whatever you need. Often that’s a lot of glue between things that other people built. Other times it’s tweaking the internals to do something a bit different.
Earlier quoted context omitted.
would you highlight some of the biggest differences between ML engineering and data engineering? I believe they're sometimes used interchangebly especially if "data" is "datasets" for ML.
Data engineers don't work with machine learning at all. In fact one of the reasons why it developed as a job title over time waas specifically to differentiate the people who work with data but don't do any statistics or ML. If a DE who is doing "datasets for ML" decides to call themselves an ML engineer, they're just getting a bit too creative with the job titles (maybe they want a career change, more money, they th…