Building the AI & MLOps community in MENA

I’m really grateful to the MLOps MENA Community for this course. It was a very valuable learning experience, and I gained a lot of new knowledge and insights that added significantly to what I already knew about MLOps.

A special thanks to Aya for being such a great instructor. Her way of explaining concepts and sharing her knowledge made the learning experience much easier to follow and genuinely enjoyable.

This course also motivated me to continue pursuing the MLOps track and go deeper into the field. I’m excited to keep learning and developing my skills in this area.

I came in knowing how to train a model and very little about what happens afterwards. The sessions closed that gap faster than months of reading on my own.

What stood out was how practical everything was — every lesson ended with something I had actually built and could put in a repository.

The study group was the part I did not expect to matter as much as it did. Being placed with people at my actual level meant the questions were the right ones, and nobody was pretending to follow along.

I shipped my first containerised model during week three, and it is still the thing I point at in interviews.

As someone moving from data analysis into engineering, I was worried the pace would leave me behind. It did not. The material assumes you are capable, not that you already know everything.

The CI/CD session alone changed how I work day to day. I had never written a workflow file before and now I maintain three.

I joined for one session on Docker and stayed for everything after it. The instructors are people who clearly do this work for a living — you can tell from the questions they anticipate before anyone asks them.

Genuinely the most useful technical community I have found in the region.

The roadmaps gave me a straight answer to a question I had been circling for a year: what do I actually learn next, and in what order.

Everything is built on free and open resources, which made it easy to start immediately rather than waiting for a training budget that was never going to arrive.

Most courses stop at “the model works on my machine”. This one started there and kept going — serving, monitoring, drift, retraining, the parts that actually decide whether a project survives contact with users.

I now understand why our models kept degrading in production and, more usefully, what to do about it.

I was the only person on my team doing anything close to MLOps, which mostly meant guessing. Having a room full of engineers solving the same problems changed that completely.

The feedback on my repository was direct and specific, and it made the work noticeably better.

Coming from a DevOps background, I expected to already know half of it. The infrastructure parts were familiar; everything about experiment tracking, model registries and drift was not.

The transition path was mapped out clearly enough that I could see exactly which of my existing skills carried over and which gaps I had to close.