I was recently diagnosed with De Quervain’s Tenosynovitis and advised to avoid typing. I took this oppurtunity (adversity?) to attend the MLOps World conference. It was the first conference of its type that I’ve attended. Unlike research conferences where the focus is on presenting novel research and industrial presence is limited to sponsor and hiring booths, this was a full-fledged industrial conference aimed primarily at industry professionals.

What is MLOps?

The first change I’ve made is that I am using the code formatter black to keep my codebase nicely formatted as I develop it. I changed the column-width to 80 but other than that I’ve let black do what it does best.

Also, since it is cumbersome to run black repeatedly, I’ve just automated it under make format in my Makefile. A simple make format before commits and my codebase stays pristine (at least formatting-wise)!

Visualizing training:

As ashamed as I am of admitting this, I’ve always been one of those people who logs their ML training metrics like losses and accuracies as console outputs. Until now. I’m now using Tensorboard with my Pytorch code and it has been an incredible change. I can predict with certainity that it is going to be a staple of my machine learning projects.

That’s all for today’s update. Hopefully I will have experimental results for next time!

Key Learnings

• MLOps tools
• Black: the uncompromising Python code formatter
• Tensorboard

Next

I plan to keep working with my research over the summer, and learn experiment management and other tools related to research engineering roles. I’ve already started with Tensorboard. Next, I plan to include Weights & Biases to track my experiments and perform hyperparameter sweeps. Since I also plan to do a code release I will be looking into configuration management as well.