Why don’t you vectorize it?”“Can I do that?”“Let’s find out.
”10.
Models built from scratch are declining (or at least you don’t need them to start)This ties back into the point about machine learning engineering merging with software engineering.
Unless your data problem is very specific, many of the major ones are quite similar, classification, regression, time series predictions, recommendations.
Services like Google’s and Microsoft’s AutoML and others are making world-class machine learning available to everyone who can upload a dataset and pick the target variable.
It’s early days but they’re gaining momentum, fast.
On the developer side of things, you’ve got libraries like fast.
ai which make state-of-the-art models available in a few lines of code and various model zoos (a collection of pre-built models) like PyTorch hub and TensorFlow hub which offer the same.
What does this mean?Knowing the basic principles of data science and machine learning is still required.
But knowing how to apply them to your problem is even more valuable.
Now there’s no reason your baselines shouldn’t be something close to, if not, state-of-the-art.
11.
Math or code?For the client problems I worked on, we were all code first.
All the machine learning and data science code was Python.
There were times I’d dabble in math through reading a paper and replicating it but 99.
9% of the time, existing frameworks had the math covered.
This isn’t saying math is unnecessary, after all, machine learning and deep learning are both forms of applied math.
Knowing at a minimum matrix manipulation, some linear algebra and calculus, particularly the chain rule is good enough to be a practitioner.
Remember, my goal wasn’t to invent a new machine learning algorithm, it was to demonstrate to a client the potential machine learning had (or didn’t have) for their business.
Side note: In beautiful timing with this article, fast.
ai have just released a new course, Deep Learning from the Foundations which goes through the math and code of deep learning from scratch.
It’s designed for people like me who are familiar with applying deep learning and machine learning but lack a math background.
To fix this, I’m going through it and it’s been immediately added to my list of favourite machine learning and data science resources.
With solid foundations, you can build your own state-of-the-art rather than iterating on a previous one.
12.
The work you did last year will probably be void next yearThis is a given.
And is becoming more so due to the merging of software engineering and machine learning engineering.
But it’s what you signed up for.
What stays the same?Frameworks will change, libraries will change, but the underlying statistics, probability, math, these things have no expiration date (even better timing with the new fast.
ai course).
The biggest challenge is still: how you apply them.
What now?There could be more but 12 is enough.
Working at Max Kelsen was the best job I ever had.
The problems were fun but the people were better.
I learned a lot.
Leaving wasn’t an easy choice but I’ve decided to put what I learned to the test on my own.
You’ll find me at the crossroads of health, technology and art.
And live on YouTube.
If you have any questions, feel free to reach out or subscribe for updates on what I’m up to.
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