There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option.

The product must be made more user-friendly. Sometimes, we have to go a roundabout way and read a lot of instruction that isn't necessary. Generally, if people use the information, they have some knowledge about it. If they don't, there should be an introduction section specifically for it. The rest of it should skip all the instructions.

We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly, it was an interesting and unique experience. 

Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience. The short session timeout is disruptive, especially across different time zones, and improving session management would contribute to a smoother development process. Enhancements in the integration with BigQuery are essential, focusing on addressing performance and usability issues, especially with large datasets. On a broader scale, enriching the UI settings within the Google Cloud Console for Datalab is crucial. This includes implementing features for unified settings in environments and facilitating easier navigation and management for end-users.

The interface should be more user-friendly.

The security should be easier to set up.

TensorBoard is available but it is hard to use.