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I think the qualifying term here is "good". I've worked with a surprising number of MLEs that don't really understand gradient descent or how most models really

I think the qualifying term here is "good". I've worked with a surprising number of MLEs that don't really understand gradient descent or how most models really work under the hood. They certainly couldn't implement most things from scratch if they needed to (neither could most data scientists). I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. The kind of engineer that always has a copy of Numerical Recipes handy, and if needed, could reimplement core components of statsmodels and sklearn in javascript. I think after this current contraction in tech is over we'll see that most of the remaining "data scientists/MLEs" will be the type of engineer I imagine an MLE to be.

itissid 4y ago
> I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. Application of Computational Stats/ML Models are not all that hard to aquire but essential. I think we need a fundamental rethink of how applied stats/ML is taught to engineers to make them effective. Here are a few things I can think of: 1. Getting a solid understanding of actually coming up with a simple enough model to do the job 2. Do Power Analysis to figure out how many samples we need. Creating datasets with Hard Negatives and overcoming sampling bias. 3. Using things like Multiple Regression to do EDA. i.e. using models as a tool vs the end goal to understand a problem space.
xxpor 4y ago
>They certainly couldn't implement most things from scratch if they needed to (neither could most data scientists). Could most CS folks actually implement Linux or Chromium from scratch?
- time_to_smile 4y ago
  I'm clearly talking about quantitative modeling tools. That said, while Linux and Chromium are massive projects each with years of development with thousands of engineers behind them, so of course it would be ridiculous to expect a single engineer to build such a thing. I also wouldn't expect an MLE to build SKLearn entirely as is from scratch on their own. However, I do certainly hope most CS folks could implement an OS or Web browser from scratch.
  - xxpor 4y ago
    >However, I do certainly hope most CS folks could implement an OS or Web browser from scratch. I am way, way less optimistic than you then. I doubt even 10% of CS grads, let alone people who have been out of school for a few years, could tell you what a page table is.

itissid 4y ago

> I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. Application of Computational Stats/ML Models are not all that hard to aquire but essential. I think we need a fundamental rethink of how applied stats/ML is taught to engineers to make them effective. Here are a few things I can think of: 1. Getting a solid understanding of actually coming up with a simple enough model to do the job 2. Do Power Analysis to figure out how many samples we need. Creating datasets with Hard Negatives and overcoming sampling bias. 3. Using things like Multiple Regression to do EDA. i.e. using models as a tool vs the end goal to understand a problem space.

xxpor 4y ago

>They certainly couldn't implement most things from scratch if they needed to (neither could most data scientists). Could most CS folks actually implement Linux or Chromium from scratch?

time_to_smile 4y ago
I'm clearly talking about quantitative modeling tools. That said, while Linux and Chromium are massive projects each with years of development with thousands of engineers behind them, so of course it would be ridiculous to expect a single engineer to build such a thing. I also wouldn't expect an MLE to build SKLearn entirely as is from scratch on their own. However, I do certainly hope most CS folks could implement an OS or Web browser from scratch.
- xxpor 4y ago
  >However, I do certainly hope most CS folks could implement an OS or Web browser from scratch. I am way, way less optimistic than you then. I doubt even 10% of CS grads, let alone people who have been out of school for a few years, could tell you what a page table is.