161–170 of 233 posts
pip install pytorch Environment broken Spend 4 hours fixing python environment pip install Pillow Something something incorrect cpu architecture for your Macbook Spend another 4 hours reinstalling everything from scratch after nuking every single mention of python pip install … oh time to go home!
> Something something incorrect cpu architecture for your Macbook
Getting my models dunked on by people who can't open MS Outlook more than 3 tries out of 5, however, have a remarkable depth and insight into their chosen domain of expertise. It's rather humbling. Collaborating with nontechnical people is oddly my favorite part of doing MLE work right now. It wasn't the case when I did basic web/db stuff. They see me as a magician. I see them as voodoo priests and priestesses. When…
Getting my models dunked on by people who can't open MS Outlook more than 3 tries out of 5, however, have a remarkable depth and insight into their chosen domain of expertise. It's rather humbling. Collaborating with nontechnical people is oddly my favorite part of doing MLE work right now. It wasn't the case when I did basic web/db stuff. They see me as a magician. I see them as voodoo priests and priestesses. When…
Collaborating with them is great, and has been a great exercise in learning how to explain complex ideas to non-technical business people. Which has the side effect of helping me get better at what I do (because you need a good understanding of a topic to be able to explain it both succinctly and accurately to others). It has also taught me to appreciate the business context and reasoning that can drive decisions about how a business uses or develops data/software.
The amount of response may be self explaining. Not my main work, but spending a lot of time gluing things together. Tweaking existing open source. Figuring out how to optimize resources, retraining models on different data sets. Trying to run poorly put together python code. Adding missing requirements files. Cleaning up data. Wondering what could in fact really be useful to solve with ML that hasn't been done years…
In a given week, I usually do the following: * 15% of my time in technical discussion meetings or 1:1's. Usually discussing ideas around a model, planning, or ML product support * 40% ML development. In the early phase of the project, I'm understanding product requirements. I discuss an ML model or algorithm that might be helpful to achieve product/business goals with my team. Then I gather existing datasets from ana…
Getting my models dunked on by people who can't open MS Outlook more than 3 tries out of 5, however, have a remarkable depth and insight into their chosen domain of expertise. It's rather humbling. Collaborating with nontechnical people is oddly my favorite part of doing MLE work right now. It wasn't the case when I did basic web/db stuff. They see me as a magician. I see them as voodoo priests and priestesses. When…
3 hours of meetings a week, that’s incredible. Sounds like your employer understands and values your time!
As we grow, I'm ever watchful for our metamorphosis into a big-dumb-company, but no symptoms yet. :)
Getting my models dunked on by people who can't open MS Outlook more than 3 tries out of 5, however, have a remarkable depth and insight into their chosen domain of expertise. It's rather humbling. Collaborating with nontechnical people is oddly my favorite part of doing MLE work right now. It wasn't the case when I did basic web/db stuff. They see me as a magician. I see them as voodoo priests and priestesses. When…
The Dead Internet Theory says most activity on the internet is by bots [1]. The Dead Privacy Theory says approximately all private data is not private; but rather is accessible on whim by any data scientist, SWE, analyst, or db admin with access to the database. [1] https://en.wikipedia.org/wiki/Dead_Internet_theory
Earlier quoted context omitted.
As somebody whose machine learning expertise consists of the first cohort of Andrew Ng's MOOC back in 2011, I'm not too surprised. One of the big takeaways I took from that experience was the importance of getting the features right.
This was very important with classical machine learning, now with deep learning, feature engineering became useless as the model can learn the relevant features by itself. However, having a quality and diverse dataset is more important now than ever.
Earlier quoted context omitted.
As somebody whose machine learning expertise consists of the first cohort of Andrew Ng's MOOC back in 2011, I'm not too surprised. One of the big takeaways I took from that experience was the importance of getting the features right.
This was very important with classical machine learning, now with deep learning, feature engineering became useless as the model can learn the relevant features by itself. However, having a quality and diverse dataset is more important now than ever.
Earlier quoted context omitted.
My answer is yes to both of those If other peoples work is reliant on yours then you should know how their part of the system transforms your inputs Similarly you should fully understand how all the inputs to your part of the system are generated No matter your coupling pattern, if you have more than 1 person product, knowing at least one level above and below your stack is a baseline expectation This is true with pe…
The parent comment had three examples...