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H2O Artificial Intelligence

H2O.ai | 1.0

Linux/Unix, Other 20170411 - 64-bit Amazon Machine Image (AMI)

Reviews from AWS Marketplace

3 AWS reviews

External reviews

13 reviews
from G2

External reviews are not included in the AWS star rating for the product.


    Retail

Good Customer support

  • April 21, 2020
  • Review provided by G2

What do you like best?
Being a user I really enjoyed the support and dedication that team shown to help us is really great!
What do you dislike?
H2O should stay ahead of the curve to give support to new technology stacks like databricks.
What problems are you solving with the product? What benefits have you realized?
We use H2O for our model building


    David Isaac M.

Good and easy

  • April 14, 2020
  • Review provided by G2

What do you like best?
Streamlining our process for generating and deploying machine learning models.
What do you dislike?
it its not much friendly when you try to automatize process
What problems are you solving with the product? What benefits have you realized?
machine learning models
Recommendations to others considering the product:
Streamlining our process for generating and deploying machine learning models.


    Young Hoon K.

H2O.ai is a great tool who wants to build a scalable and interpretable machine learning pipeline.

  • April 14, 2020
  • Review verified by G2

What do you like best?
I find it most helpful that it provides GUI for users to estimate how long the model will run as well as the water meter showing how the resources are used along the modeling process.
What do you dislike?
H2O Frames have very limited data processing options compared to python pandas or pyspark dataframes. If we can have more data maneuverability, I think it would help a lot.
What problems are you solving with the product? What benefits have you realized?
Purchase forecasting, employee estimation, etc.
Recommendations to others considering the product:
I believe it is a great tool for those who are looking to establish ML pipeline with ease.


    Marom M.

Excellent software - great addition to a data science team

  • April 13, 2020
  • Review provided by G2

What do you like best?
Excellent support. Useful and reliable software. Easy to deploy.
What do you dislike?
No issues. The software works very well.
What problems are you solving with the product? What benefits have you realized?
Predictive analytics in credit and marketing. Software works well in our systems.


    Insurance

Expanding AI Product with Excellent Support

  • February 22, 2020
  • Review provided by G2

What do you like best?
The predictive modeling and machine learning capabilities of this product are top-notch along with their support and training.
What do you dislike?
Documentation in general can be improved.
What problems are you solving with the product? What benefits have you realized?
We have been able to create features previously undetected and build more accurate prediction models.
Recommendations to others considering the product:
Ask for H2O's training before you use the software - you'll have a much better time.


    Marc S.

Excellent machine learning tools

  • February 20, 2020
  • Review provided by G2

What do you like best?
Good integration between development and production. Simple and powerful visual interface to complex backend. Very rapid iteration of products, continuous improvement.
What do you dislike?
Price is high for closed source product, Driverless AI. It's worth it if you have a serious application, but the cost is an impediment to early adoption.
What problems are you solving with the product? What benefits have you realized?
Streamlining our process for generating and deploying machine learning models.


    Industrial Automation

DriverlessAI

  • February 20, 2020
  • Review provided by G2

What do you like best?
H2O provides DriverlessAI for an efficient AutoML platform and effective UI tools for data scientist and end-user.
What do you dislike?
DriverlessAI could provide more use-cases in Manufacturing domain.
What problems are you solving with the product? What benefits have you realized?
We are working on Predictive Maintenance and Analytics, which could be potentially used for Manufacturing and Factory.


    Marketing and Advertising

Great AI product

  • February 20, 2020
  • Review verified by G2

What do you like best?
Many model types, great cluster performance, easy to setup
What do you dislike?
Would like to see SVM , Gaussian processes, and some time series models
What problems are you solving with the product? What benefits have you realized?
AdTech modeling. Can create complex models on data sets with a billion rows. Much faster turnaround time


    MvpOfMac4841

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

  • January 08, 2019
  • Review verified by AWS Marketplace

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.


    Rahul K.

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

  • December 13, 2018
  • Review verified by AWS Marketplace

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.