Good points 1. You can learn practical approaches to fairly deep and realistic topics in a short period of time. 2. You can see useful tools such as wandb, wit, and implementation processes such as docker, kubernetes, kubeflow, mlflow, and bentoML. 3. You can obtain practical/practical approaches or know-how. Bad points 1. It is not a lecture just for 'Inflearn lectures', but it seems to have been recorded and uploaded as is from other talks? or lectures? => There are many distracting parts, and the lecture recording quality is a bit disappointing compared to other lectures for the same price. I purchased and listened to quite a few lectures from Inflearn, and watched lectures on other platforms, but if you only look at the lecture quality, it would be difficult to get a good evaluation. 2. Continuing from 1, the unrefined explanations/personal opinions/lecture materials are disappointing. It seems like something that could happen because it wasn't filmed just for 'Inflearn'. (I think Inflearn doesn't do any pre-review of the lecture content at all.) 3. Since you have to leave a course review to receive the lecture materials, it's difficult to follow along while watching the video. Of course, it might be different if you write a course review before completing the course, but I personally don't want to leave a useless review, so I plan to leave a review after completing the course and watch the lecture materials while listening to it repeatedly. Overall As it was announced that the lecture target is 'intermediate or higher', I don't recommend it if you don't have some experience or have heard of it. However, it's a good opportunity to quickly look through it while you have experience and see the approach from a practitioner's perspective. Also, if you're someone who wants to study machine learning/deep learning models diligently and acquire MLOps skills, it's not bad to listen to it once. If you take this course and also complete the MLOps course that recently opened on Coursera, it seems like a good combination.