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H2O Artificial Intelligence

H2O supports the following distributed algorithms: GLM, Naive Bayes, Distributed Random Forest, Gradient Boosting Machine, Deep Neural Networks, Deep learning, K-means , PCA, Generalized Low Rank Models, Anomaly Detection, Autoencoders See more

Customer Reviews

3
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The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm, but the interpretability module has room for improvement

  • By MvpOfMac4841
  • on 01/08/2019

Our primary use case is machine learning.
How has it helped my organization?
It has enabled our work force to be more efficient.
What is most valuable?
One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm. It also gives you an interpretability capability which allows you to have some understanding of what's inside the algorithm and why it's behaving a certain way, making sure you are not bias towards the outcome.
What needs improvement?
The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.
I would like more support for scalability and deep learning. Right now, they are very strong in supervise and supervise learning, but not in deep learning. I'd like to see them be more well-rounded, where they have support for deep learning, but I'm not sure that is their business model.
For how long have I used the solution?
One to three years.
What do I think about the stability of the solution?
In terms of the stress we put on it, it is still in the very early days for us to actually take it through its phases.
What do I think about the scalability of the solution?
It does appear to scale. We have very large use cases. The product scales as advertised.
How is customer service and technical support?
They have excellent tech support.
How was the initial setup?
It was fairly easy to set up, then get up and running.
Which other solutions did I evaluate?
It was already selected. I don't know what process the company went through.
What other advice do I have?
Do your due diligence, making sure with your use cases, this is the right product for you.
Directionally, they are headed in the right place. They're also putting a lot of muscle behind it, but they're very focused in one area. Supervised on supervised learning is the market that they're going after. If that's their strategy, then they'll get some part of the market, but they'll leave the other part of the market behind.
We use just the AWS version of the product.


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

  • By Rahul K.
  • on 12/13/2018

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.


A great way of launching an H2o cluster!

  • By Robert
  • on 11/20/2017

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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