I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services.  But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite. 

It could be more clear, and sometimes there are errors that I don't quite understand. So maybe they could improve the pricing. The performance could also be better.

Customizations are very difficult, and they take time. The product should provide better customization. It should provide some out-of-the-box features that can be used to create a field or to make changes in the data validation. Linking to the database should be crisper so that we can do it with simple drag-and-drop functionalities instead of coding.

The solution can be improved by simplifying the process to make your own models. Currently, I am using lower-end cameras and they can not recognize every object. The solution can take a page out of Microsoft's AI within Office 365 to make it more user-friendly. Allowing the solution to directly recognize lists so they don't have to be written out would make it more useful.

At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning.

One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform. I see room for improvement in the aforementioned area.

Currently, I think the generative AI isn't quite a boost, and Azure is working a lot with it, so I think Google should incorporate it by considering it as a bit of an eye-opener towards their own side. That can put them at a greater advantage compared to when they attempt to integrate themselves indirectly into the pipeline. They also provide PPU servers as well as their distributed server, which are not currently available in the pipeline. If they are able to incorporate the aforementioned details for improvement, then they will be able to lead.

One more thing that you can like is when you have a production environment and development environment, and I want to push my modules from the development environment to the production environment, it is not currently possible in Google Cloud AI Platform. If I have two projects where one is the development and the other one is the deployment, and if I try to pass my modules from the module registry, it is not possible right now. That is where it lacks. I sent a request to the support team regarding an issue that we were trying to pass to the development project and how it is not possible yet.