H2O Artificial Intelligence
H2O.ai | 1.0Linux/Unix, Other 20170411 - 64-bit Amazon Machine Image (AMI)
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It is helpful, intuitive, and easy to use. The learning curve is not too steep.
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.
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Great platform for R
What do you like best about the product?
It's a great platform with R library. Library functions saved our time.
What do you dislike about the product?
It needs more R libraries on planning management.
What problems is the product solving and how is that benefiting you?
We've used H20.ai for assortment optimization.
Popular machine learning platform
What do you like best about the product?
I met h2o.ai with Gartner Magic Quadrant. It's great ML platform with it's extensive built-in library.
What do you dislike about the product?
Price architecture is confusing a bit. Also we'd like to see more use cases and customer stories for marketing applications.
What problems is the product solving and how is that benefiting you?
We evaluated churn prediction algorithms to score our customer database.
Very easy to use
What do you like best about the product?
the tool itself is very intuitive and easy to use.
installation is quick.
porting to R and Python options are nice to haves.
installation is quick.
porting to R and Python options are nice to haves.
What do you dislike about the product?
there isn't much visibility into how h2o is better in using each algorithm.
What problems is the product solving and how is that benefiting you?
machine learning. expediting the process and replicating the process.
Quick Prototyping
What do you like best about the product?
It's a great tool for really quick prototyping. Easy to build pretty complex models fast and test the results on datasets. It allows you to test various models before you decide which you want to fine tune. Good python support as well.
What do you dislike about the product?
Documentation can be improved especially with how to integrate with python.
What problems is the product solving and how is that benefiting you?
Prototypes for ML application in software engineering.
Bien mais un peu déçu
What do you like best about the product?
Le moteur steam AI: une plate-forme de bout en bout qui rationalise l'ensemble du processus de création et de déploiement d'applications intelligentes il est rapide
What do you dislike about the product?
Certain beug et ralentissement qui sont tres gênent
What problems is the product solving and how is that benefiting you?
Fais son travail
Recommendations to others considering the product:
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A great way of launching an H2o cluster!
This product has facilitated the process of launching a connected group of instances which are ready-to-go for h2o. Setting up a new cluster went from 30-60 minutes to less than 5.
Awesome work, looking forward to seeing new updates!
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