After school ended I thought I was in a decent place — some money in the bank, only a little bit of debt on my student loans (let’s go Joe and Bernie, get Manchin on board and get those loans forgiven already), but I can’t believe my luck. I’ve landed at the perfect place for me: The Rapid Growth Institute — a sort of think-tank/consultancy that works with the biggest companies in tech to help them solve problems and grow their market share. Going into my degree I knew machine learning was something on the rise, but even in the past 4 years things have changed so much. When I started at UKD (the University of Kansas at Delmont, go Tigers!) in Computer Science I knew I wanted to do the Software Engineering concentration so I could get into the AI industry; I knew AI was on the up. There’s some people who say you need to do graduate studies, like a Masters degree, to really find a spot in AI, but now with the applied AI tools out there — TensorFlow, Scikit.learn, Keras, and all the other ones — they’re all so easy to use these days all you really need to know is how to import a library and change a few lines in the sample code they provide. The open source AI movement has been great to get all these tools out there so people can start using them to make some money and help our economy get back revving up!
If you’re just getting started in your degree, here are my three biggest tips to find Machine Learning success:
- Try, try, try! If you can’t get an ML package to work, just download and try another. The time you waste trying to figure out problems isn’t worth it if you can just try something else and get it to work. There’s always another piece of sample code or another platform to try.
- Good data is important, but the data you have in your hands is even more important. You can always find better data, but you have to know that getting first to market is more important than getting good data.
- Just build stuff. Get your hands dirty and make some messes. You’ll only get ahead if you move fast and stay on top of the latest trends in AI.
By following those tips I was able to have a great portfolio of work that I showed at all of my interviews once I graduated. I even had a job offer before my senior year, but I decided to stick it out. Demo projects are perfect for job interviews. With COVID and the pandemic they were mostly done over ZOOM, but a free github page can go a long way these days. Jupyter Notebooks were also great to share so I could talk my way through models and graphs with the companies I interviewed with. To be honest, it seemed like if you had any kind of experience with AI you had a real leg-up. Tech is just exploding these days, so the demand for talent has never been higher! California, here I come (once all this Omicron stuff is over and I’m back in an office). A hidden bonus of AI working mostly in the cloud is that you can work from anywhere.
I’ll be writing as much as I can about my experiences at RGI, within the limits of my NDA of course, so make sure to hit that subscribe button to get the latest as it comes down the (ML) pipeline. Hit me up with any AI new-hire questions in the comments section! Especially if you’re a UKD alum!