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Dec 13 2018
What is most valuable?It is helpful, intuitive, and easy to use. The learning curve is not too steep.
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… more»
What needs improvement?The model management features could be improved.
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… more»
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… more»
Dec 26 2019
What is most valuable?The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people.
What needs improvement?On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem. I would like to see better integration with Python and data science capabilities.
Which solution did I use previously and why did I switch?I have worked with similar solutions but most of them have been custom. Not a single service could provide us with so many things. For example, H2O plus Spark, or H2O plus Sparkling Water.
What other advice do I have?H2O is a good product, and I suggest that people use it. My advice to anybody who is considering this type of solution is to consider whether they want to procure such products and use them versus building something custom. It depends on time availability. It really just exposes Spark as the next… more»
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Jan 08 2019
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
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… more»
How has it helped my organization?It has enabled our work force to be more efficient.
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… more»
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… more»
Which other solutions did I evaluate?It was already selected. I don't know what process the company went through.
Apr 05 2018
Provides fast training, memory-efficient DataFrame manipulation, well-documented and easy-to-use algorithms
What is most valuable?* Fast training * Memory-efficient DataFrame manipulation * Well-documented, easy-to-use algorithms * Ability to integrate with enterprise Java apps (through POJO/MOJO)… more»
How has it helped my organization?We previously needed a four-machine Spark cluster to be able to train an ML model using tens of millions of transactions, and hours of time during the modeling phase… more»
What needs improvement?Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.
What's my experience with pricing, setup cost, and licensing?Currently, we do not purchase enterprise support.
Which solution did I use previously and why did I switch?We used to developing on Scala + Spark ML. We switched, at least in part, due to reasons mentioned in the Valuable Features section of this review.
What other advice do I have?We rate it at eight out of 10. It is very fast, light-weight, well-documented, and low-maintenance. The reasons it is not rated 10 are, it lacks the data manipulation… more»
Which other solutions did I evaluate?We have experience with pretty much everything available; hence, the switch was an informed decision and natural.
Jan 02 2019
There is an ease of use when connecting it to our cluster machines. I would like to see more features related to deployment.
What do you think of H2O.ai?
What is our primary use case?We use it for building models with large amounts of data.
How has it helped my organization?We are using it for prototype projects. We have not deployed it.
What is most valuable?The ease of use in connecting to our cluster machines.
What needs improvement?I would like to see more features related to deployment.
For how long have I used the solution?Trial/evaluations only.
What do I think about the stability of the solution?As this was just for a prototype, we did not stress the product too much.
What do I think about the scalability of the solution?For the use case that I had, I did not run into any scaling issues. Therefore, I worked well for scalability.
How is customer service and technical support?I didn't run into any…
Feb 14 2019
We're hoping to save costs on internal development but keep enough flexibility to choose ML techniques and performance indicators
What do you think of H2O.ai?
What is our primary use case?The idea is to migrate the current model's development practice to another platform. Then after, try to create a proprietary platform using R and Python. The company is interested in using an external platform in order to have an updated environment.
How has it helped my organization?Still on it. The idea is to save the cost of internal development but keeping enough flexibility to choose ML techniques and performance indicators.
What is most valuable?Still on it.
What needs improvement?Feature engineering.
For how long have I used the solution?Still implementing.
May 02 2018
What do you think of H2O.ai?
What is our primary use case?Testing/modeling data in the initial stages of approaching a machine-learning problem. Environment: Laptops running Ubuntu 16.04/Python 3.
What is most valuable?AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms; with training input data.
What needs improvement?It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.
For how long have I used the solution?Less than one year.
User Assessments By Topic About H2O.ai
What is H2O.ai?
H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.
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