The demand to implement machine learning continues to outpace the supply of machine learning engineers. And in my day job, there is a strong push to use machine learning as heavily as realistically reasonable*
Yes, ML is sexy, but often it’s overkill. The first rule of Machine Learning is, if you can solve the problem without ML, that’s the better solution. ML can then be used after the solution has found traction to improve the customer experience. You can find all of Google’s 43 Rules of Machine Learning here.
Part of my professional growth path is to go from being basically proficient in Machine Learning to being recognized. So I borrowed and tweaked a learning path towards earning Tensorflow Developer Certificate and took a long weekend to brush up on Tensorflow and earned the Tensorflow Dev Certificate from DeepLearning.AI.
I’m still working through the Hands on ML book I cracked earlier, but the course was a nice interactive (and certified) experience that gave me a few working notebooks I plan to expand on.
In addition to that, over this past weekend, I added a Projects section to this site so I can link to various projects and continue getting practice in Django. All in all, I feel like the repeated and constant exposure and repetition is paying off. I'm gaining confidence not because I am not making errors, but because when I do encounter something, I'm able to google my way out of it.