Currently, we're currently provisioning some ML models into Vertex AI. The sales team will likely start using the other resources in a month or two. 

When deploying the model on-premises, with a global team, we faced time lag issues. On Google Cloud, there is a facility to select different locations, making the application available on servers almost anywhere. That's why we shifted to the cloud.

It's a data science platform, and we use various resources from Google Cloud, depending on project permissions.

The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.

Our main use cases involve transferring workloads from AWS and Univision to Google Cloud Datalab. Before coming to the setting we utilised Google Datalab for looker and handling separated tables for research and development scenarios. Currently in association with AWS and Univision, we are focusing and migrating, defining data set tables and moving data to the Google hosted platform.

We use it to perform Exploratory Data Analysis on our dataset, which focuses on machine learning. Through Datalab, we conduct an in-depth examination of the data, identifying key features for our machine-learning models. Additionally, we leverage it to enhance data quality by addressing missing values, applying preprocessing techniques, and ensuring that the data aligns with the specific requirements of our customers. This involves employing statistical methods to analyze data patterns and make informed decisions about data processing.

We are using this solution to help manage personnel and to see if everyone is in the right place.