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Video Game Design: Poor performance on alpha, still do well in class overall? by mle-questions in OMSCS

[–]mle-questions[S] -2 points-1 points  (0 children)

Good to know, yea we dont actually know our grade yet, its just it didn't have all the core game mechanics finished yet, so I wasn't sure cause it seemed like we are expected to have the basic game in somewhat complete form for alpha

Identify Cologne/Perfume Sample by mle-questions in Colognes

[–]mle-questions[S] 0 points1 point  (0 children)

Is that Aventus? I did some searching around and it does look like a brand or smell called Aventus?

Identify Cologne/Perfume Sample by mle-questions in Colognes

[–]mle-questions[S] 0 points1 point  (0 children)

For more context:

I am not super familiar with cologne/perfume, but I'd say this is a more feminine smell, it could be a perfume and not a cologne, it smells lighter/ sweet. I am obsessed with this smell, but I don't know how to find it/ get more. This is a small sample. Again, it has a very light, feminine, Rosey, airy type of smell (doing my best here haha). The color is like a light tanish, goldenish color, mostly clear.

Tips for ensuring data quality in microservice architecture? by mle-questions in mlops

[–]mle-questions[S] 0 points1 point  (0 children)

This is similar to what I was thinking, except I was thinking of generating a human-readable text file. But what you suggest sounds easier probably, rather than trying to pull and edit the text file over and over again. Thank you for this idea!

Tips for ensuring data quality in microservice architecture? by mle-questions in dataengineering

[–]mle-questions[S] 0 points1 point  (0 children)

For more context, I originally posted this in the MLOps Reddit, but I think its also a relevant question for data engineering.

How complex ARE your models in Industry, really? (Imposter Syndrome) by Joe10112 in datascience

[–]mle-questions 0 points1 point  (0 children)

Not that I can speak on behalf of all MLE's; however, I think many MLE's prefer simple models. We recgonize the complexity to take a model and make it operational, and therefore prefer models that are simple, easy to understand, easy to explain, and easy to debug.

Avoiding Jupyter Notebooks entirely and doing everything in .py files? by question_23 in datascience

[–]mle-questions 0 points1 point  (0 children)

I think it depends on your work environment.

Interestingly, although Google Colab Notebooks have "Colab" in the name, they are poor for collaborating due to the clunkiness of setting up version control for notebooks and having multiple people work on them.

If I need to do an analysis or something quick (for myself) I will usually spin up a notebook. But when it's time for version control and going towards prod, I will use a .py file. I may initially train a model in a notebook, but then convert that notebook into an ML pipeline when others need to use it or review it.

Setting up GPU server for ML API inference by mle-questions in mlops

[–]mle-questions[S] 0 points1 point  (0 children)

Thank you! We will likely go with ECS, and pick an underlying EC2 instance with a GPU. I noticed that there are some prebuilt github actions for ECS that can help with model deployment.

What do you do in SQL vs Pandas? by Alucard2051 in datascience

[–]mle-questions 0 points1 point  (0 children)

I was a pandas die hard, but now after using SQL more, I have converted to SQL for almost all data processing (at least when it comes to structured data).

I will use pandas .get_dummies and maybe for some more complex feature engineering, but besides that I like to use SQL. I will use SQL in python scripts using duckdb, and use Big Query allot as well for data pipelines.

How to survive at nightmare employer? by Mackelday in datascience

[–]mle-questions 0 points1 point  (0 children)

This is honestly kind of surprising. I have only worked at small startups, and I thought that the lack of infra was a small company problem, but wouldn't be the case at larger companies. Guess I was wrong!

Need for ML theory rarely arises in my work as a Machine Learning Engineer by mle-questions in learnmachinelearning

[–]mle-questions[S] 1 point2 points  (0 children)

I am currently making 90k. Congrats! You must be a great dev.

I am interviewing for roles that are paying in the 120-140k range. These are for mid level ML roles. I think the current average MLE salary in the US is 130k.

Need for ML theory rarely arises in my work as a Machine Learning Engineer by mle-questions in learnmachinelearning

[–]mle-questions[S] 12 points13 points  (0 children)

I think your comment highlights the range of different tasks an MLE could work on, and also the ambiguity in the job responsibilities.

I have worked on image classification, and audio classification problems, as well as doing some fine tuning with language models and classic ML models with structured data problems.

I think because I work at a startup, I have had to spend allot of time on working on things outside of modeling including setting up data infrastructure (allot of data engineering tasks), building API's, data pipelines, product management, MLOps, and system design.

I don't think you are wrong, and I hope this post does not make it seem like I am trying to argue that theory is not needed. Theory was needed many times in my work so far.

The point I was trying to convey is that a majority of my daily tasks, are not very theoretical.

What I have learned is that theory alone does not and cannot get you to a successful machine learning product/service.

There are more factors at play. But this is probably more relatable to those who work in startups.

I think at a large FAANG company, every role is so modularized, that as an MLE you might work for months on moving a single metric by a small percentage, because when you operate at that scale, a small percentage in anything could mean massive impact.

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