Machine Learning All The Things
I went to re:Invent in Las Vegas last week, and it was fantastic. This was my first time going, even though I’ve been a software engineer for quite some time (nearly two decades). One thing really stood out to me:
Machine learning was either the centerpiece or tightly integrated with whatever was going on, everywhere I looked.
I went to the expos and got swag and beer. I went to the sessions and got detailed information on various topics. I watched the keynotes on video because I didn’t get reserved seats in time (also this way I could pause the 1.5–3 hour talks to use the restroom).
There was so much machine learning I was actually surprised. I was surprised because machine learning isn’t exactly a new kid on the block. Data science, machine learning, artificial intelligence, and natural language processing have been heavy hitting technologies for several years now.
This difference this year, I think, is that we have so many usable products and services leveraging all this cool tech. I’m not just talking about the keynotes. When walking around the expos both at the Venetian and the Aria, I saw SaaS companies that either heavily leverage machine learning in their product offering, or provide data science services for you so you don’t have to hire the talent yourself.
You should learn machine learning.
If you haven’t already started to learn machine learning, start now. It doesn’t matter what kind of developer you are now…you should get to know machine learning.
Whenever there is some hot new tech trend that comes out, it’s cool to take a look at it casually. Tech is fun, after all.
This, however, is not just some new trend. This is a driving force. Just like you had to learn databases years ago because they drove every website and app out there, you need to be able to hold a competent conversation about machine learning.
Even if you never work as an engineer spending your days solidly in ML/AI, you’ll likely work with developers who do spend their days like that. If not now, then just give it five years.
It will pay to know the field.
So how do you learn about this stuff?
Fortunately, you don’t need to invest a lot of money (or even any) to learn about this. There is free content everywhere.
YouTube. Medium. Just search Google as questions pop into your head.
Currently, I’m using Kaggle.com and reading a couple of books (a small investment, but I like books) on ML.
How are your math skills? If you just want to learn about ML, but not how to do it, some math terminology knowledge should be sufficient. If you think you want to work in this field, however, it looks like you may need to make sure your math skills are shored up.
Then, just treat it like learning any other language, framework, or skill. Sit down and code.
Why bother with ML?
Can you get by with just being a full stack developer, backend engineer, or database developer your entire career? Yes, I think that’s possible.
But I don’t think that’s wise.
Careers in technology are always evolving. Always growing. You’ve got to grow and keep up, otherwise, you may not be able to compete for the good jobs at good companies.
If my time at re:Invent this year taught me anything, it’s that machine learning is a skill to have. Don’t wait until your non-technical boss (or their boss) comes to you and asks how to implement machine learning, data science, or AI into your workflow.
Start learning now. And if you’ve already started, get good at it.
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