It's a host of use cases depending on, again, the the client requirement. 

I used Google Cloud AI Platform for a project at AltaML, where I had to predict client satisfaction levels for a particular company. At that time, I didn't have experience with APIs or anything like that, so my experience with the platform was limited. I think Azure has more options for me since they have a design feature that Google doesn't have.

I work for an IT company, and we use the solution to build applications for different customers.

The primary use case for this solution is to read handwritten lists and input them into Excel files. I also use the solution to read scannable material to determine if it is still usable.

We are working on PIA data, which is in encrypted format. We get a set of records for each individual. This record size could be 10 to 50 for each individual. We need to identify the best email and phone number out of these set of records. We are developing a sense data model for that and along with the PIA attributes for each individual, we also take help from different sources – like AT&T and T-Mobile – that are going to provide telecom services. We take data from these providers for validation purposes.

We don't consume their data, but we use their data for validation. We bounce our data into their records and they say, "This this is correct," or "This is not correct."

On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients.

The clients, in a sense, want to validate their data if there could be chances that it's stale in nature. They want to make sure that their records of individuals are up-to-date.

We are using the latest version on the public cloud and deploying it on GCP.

I like to develop inside platforms like Vertex and Google. I use such specific things to create and develop my skills.

Recently, I was using QFlow for generating the pipelines, and in the recent version, I originally worked with Google Cloud AI Platform, along with Vertex.

With Google Cloud AI Platform, I tried to create the end-to-end pipelines and adopt pipelines using Vertex for incorporating all the features from serving online endpoints along with past predictions.

In the past, I have deployed the solution for government clients wanting cloud applications and databases services.