I’m working on my AutoML startup since 2016. Recently, I’ve come to the conclusion that AutoML is broken. AutoML should make deploying ML easier and faster, I think it is the opposite. What is more, I think it can do more harm than good.

The benefits of AutoML:

1. AutoML can train (some) model for provided input data. Nice!
2. AutoML can be used for computing batch predictions for provided test data.
3. AutoML can be used for deploying the model behind REST API. User can access model with REST requests. Nice 🙂

The boring code – hidden part needed to make AutoML work:

1. In real-life someone needs to create the input dataset. There is a need to write some code which will fetch data from multiple sources, merge and create a data frame. This code needs to be well written, so all software engineering good practices should be applied.
2. If the final ML model will be used in production, then more code needs to be created, for preparing data for predictions. Whether you are going to use batch predictions or use REST API, you need to add more code.
3. Most of the AutoML solutions I know, do not provide detailed model maintenance mode. There should be monitoring of model and monitoring for input data quality. Based on this monitoring, there should be an automated decision when to update/retrain the model. The feedback of model decisions should be stored somewhere. If you go to production with AutoML, probably there will be a need to write additional code for maintenance and monitoring.

I think there is a lot of boring-code needed to use AutoML in real-life in the production.

The last thing I don’t like in AutoML is complexity. When you apply ML to the problem even a simple model should bring you huge improvement (ROI). I like simple models because they are easy to understand. Applying a complex model as a first iteration for solving a business case with ML is unnecessary over-complication.

If you want to read more, I wrote a post about my thoughts about broken AutoML: https://pplonski.github.io/automatic-machine-learning-is-broken/

I have just woke up (literally its 4:45am here) from a dream where I have built a “genuine AI” based on optoelectronic hardware. I call it genuine because 1) it had some kind of personality, 2) it trained itself from scratch and in unsupervised manner.

– What is your name?
– Sania

Sometimes this AI provided less articulate answers:

– What is my name?
– Math fails to give a definitive answer

Which I suppose was an equivalent to “I don’t know”.

Which other things do you think I have asked?

P. S. Depending on its ‘mood’, this AI sometimes called itself Donnie

Throwaway for obvious reasons.

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Background: I am currently finishing up a PhD in physics (at a top 10 place, if that matters) and got into ML two years ago. I have been lucky enough to collaborate with some ML researchers at my institution and have published a few (~4) papers at top conferences. Two of them are first author papers, but none have really gotten that much attention (I do have several hundred citations but mostly for my physics publications).

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I have spent the last few months looking for research scientist positions at industry labs. Unfortunately I was unable to secure a research scientist position, but was recently offered a “postdoc” position at a well-known lab. Basically they said that that they want to hire me in this position for a year and then potentially promote me to a “research scientist” position depending on my performance.

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I was surprised since I didn’t know postdoc positions even existed in industry (but they told me that this type of position is typically offered to students who are taking a year off before becoming faculty). While the salary is not too bad (~130K), they are giving me no stock. My ML PhD friends have told me that you can typically expect 100K+ in stock that vests over a few years when you join as a reserach scientist with a industy research lab. So I am not too happy in terms of total compensation.

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I do have another job lined up (quant finance) that pays substantially more (300K+), but it is not a research position. Any advice on what to do? Is there a chance that the industry lab would consider giving stock to a “postdoc”? Anyone else have taken such a postdoc position?

Hello Guys, I need hel with some data. I have a multivariate time series that have 6k columns and samples with different size. The data are collected per second. However I have samples with 5 hours and others samples with 10 minutos. I already thought about padding, but is this case, the padding part is too big that can downgrade accuracy of RNN. There are articles using another type of solution for this situation?

Hey all! Just sharing this article on linear regression theory – all that’s needed before a data science interview. Hope it helps!

https://towardsdatascience.com/all-you-need-to-know-about-linear-regression-to-be-interview-ready-fc58a00a0b8c