Our primary use case is for data science. Some of our data scientists use it pretty heavily to build models.

How has it helped my organization?

One example, we are able to automate life insurance. We have to underwrite policies. When somebody applies for a policy, we take their blood, then assign them a risk: substandard, standard, preferred, etc. Depending on this, we price our products. Usually the process is that you take the blood, then it goes to a lab and we get the lab results back, then an underwriter takes a look at the lab results. This is usually done in a two week time frame to get a rating. We were able to build models to automate all of this, and now, it happens in real-time. Somebody can apply online and get issued a policy right away.

What is most valuable?

It is helpful, intuitive, and easy to use. The learning curve is not too steep.

What needs improvement?

The model management features could be improved.

For how long have I used the solution?

Three to five years.

What do I think about the stability of the solution?

We haven't put a lot of stress on it.

What do I think about the scalability of the solution?

The size of the environment for my database is probably about 900TB.

So far, the product has been good from a scalability prospective.

How is customer service and technical support?

I would rate the technical support as an eight out of ten.

How was the initial setup?

The integration and configuration were good. I would rate them as an eight out of ten.

What was our ROI?

We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.

Which other solutions did I evaluate?

We looked at Amazon SageMaker on AWS.

This product still was open source at that point, then we did get proprietary support after that. The other products were not open source, and we couldn't really try them out beforehand to see if we liked them or not.

H2O.ai is a great product for data scientists in general. It has a lot of options and is really flexible. Also, the pricing was good.

What other advice do I have?

H2O.ai works directly with a lot of our cloud data, big data environment, and Amazon RedShift environment. The big data integration was easier from a performance perspective than Amazon RedShift. That is because our big data environment is still on-premise vs RedShift, which is on the cloud, so we had to go through some struggles to get it operating with RedShift.