Google Cloud AI Platform Reviews

I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.

The most valuable feature I find is Firebase. Within Firebase, there are a couple of databases. Also, within Google, there is BigQuery, which is very valuable. The feedback left about these tools was really helpful and informative for us.

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.

I find the handwritten feature that recognizes handwriting text to be the most valuable feature.

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

A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick. 

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

In comparison to Salesforce, the product is much cheaper. So it is generally preferred by our clients. Since they are using it, we end up using it as well.

We use Google Cloud AI Platform to extract text from images, such as forms. 

The platform's Google Vision API is particularly valuable. It helps with text extraction from images. 

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 think that the Google Cloud AI Platform has some really great options, including the ability to work with video and classify objects within it. I was particularly interested in how the platform functions and its capabilities in this area.

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.

We are trying our best to improve our existing models and privacy and to keep on updating it, and also we are trying to use reinforcement learning and separate APIs so that if a user wants to update their data, they can do so.

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.

I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms. Apart from that, I am a bit confused regarding what it provides apart from since we have alternatives like SageMaker or Azure ML. I need to know what I can achieve through it.