MLOps Notes 1: The Machine Learning Lifecycle
Hello, everyone! And Happy New Year!
Akhil Theerthala here. Because of my personal obligations, I haven’t been able to write articles seriously until now. But finally, beginning today, I will start contributing once a week to my medium page!
I have yet to take any online courses in the last two months because I’ve been swamped with work. But now that I’ve started taking the Machine Learning For Engineering (MLOps) Specialization on Coursera. I’ll be sharing my notes on my medium. So, for the time being, check back every few days to catch up on my notes!
What is this course about?
Till now, we have seen the training part of machine learning models. Now it is time for us to see how we put them into production and the challenges and requirements we face in the process. i.e., this course deals with what happens outside the Jupyter notebook.
Example: Let us look at the case of scratch detection of mobile phones. We use a device to identify whether the phone has scratches or not.
- Edge Device: device living inside the factories
- Software: The program that controls the way edge device works.
In our case, we have inspection software that controls the camera, takes photos, and passes…
