Content well written. Some lab issues. Resolved but frustrating. Language processing a bit off on transcribed material from speakers.

The course is quite educational, yet the lab material can sometimes be confusing, especially for beginner users

It's ok. There are example notebooks to understand the code. The pricing part is missing.

Good starter on basic MLOps on GCP for those who want a quick dive and a hands on project

It was good experience learning about the deployment process of ML application on GCP.

I miss a chapter on automating monitoring models when metrics diverge

Needs to cover the subject in greater detail

good enough but lab is not enabled

It's a good tutorial for beginner

Good learning experience.

The labs are so important

learned something new

Material is good

Good Course

great!!

good

The course gives a nice overview, but either it should be more generic and fun, or more detailed and techy but also longer. Now it feels like its trying to do both and failing at it. It is a bit too condensed and boring on the practical parts, and most of the tasks can be solved with copy paste, and somehow I don't feel that the whole course motivated me into stop copy-pasting and instead actually learn these things. Several of the Qliklab workshops seem to be buggy.

The content is well designed and explained. The Hands-on Lab sessions need a lot of improvement. MLOps is implemented in a really complex manner (but that is more about a comparison between GCP and other providers). But for ramping up MLOps on GCP, this course is a really good starting point. Best of Luck!

A good course to get started with MLOps. The reason why I think the course deserves not more than 3 stars is that it lacks the depth that is needed to aid a better understanding of the concepts and components discussed. Overall, it's a good place to start and gain intuition about MLOps.

I took this so I could get better at Kubeflow on EKS (not Google Cloud) and it was not worth it. The Beginning is promising and the explanation of kubernetes was great, but then it quickly became not applicable. If you are using it for GCP then it may be worth while.

The labs are too basic, I expected to view how to use tools such as tensorboard with kfp, with the intention to track progress of the models. But more relevant is the lack of examples on how to train/hyperparameter-tunning using a kfp alone avoiding AI jobs tool.

Even though class was taught by instructors from Google, the quality of tech around it was not Google-like. The labs in two week have serious issues once the pre-requisite steps are complete and experimental/fun//learning part of the lab begins.

Donne une bonne vue théorique du MLOps sur GCP mais la pratique est moyenne. Il manque un réel cas d'étude pour solidifier les acquis.

good content, but labs tend. To fail and debugging/support is not fantastic, forums dont have so. Many posts to support Either.

The Labs took a lot of the promise the course had. It was a good time overall. Learnt a lot that requires further attention.