H2O.ai Other Advice
RK
Rahul Koduru
Director of Data Engineering at Transamerica
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.
We also use the on-premise version.
View full review »DC
MvpOfMac4841
Managing VP of Machine Learning at a financial services firm with 10,001+ employees
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 integrates well with our notebooks. It also integrates well with our homegrown tool sets.
View full review »AS
ArnabSen
Associate Principal at a consultancy with 501-1,000 employees
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 layer.
I would rate this solution a seven out of ten.
View full review »Buyer's Guide
Data Science Platforms
March 2024
Find out what your peers are saying about H2O.ai, Knime, Dataiku and others in Data Science Platforms. Updated: March 2024.
765,234 professionals have used our research since 2012.
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 capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O.
View full review »MH
DataScie1afc
Data Scientist with 51-200 employees
It deals well with its core functionality. The product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases.
I am using the on-premise version.
View full review »Buyer's Guide
Data Science Platforms
March 2024
Find out what your peers are saying about H2O.ai, Knime, Dataiku and others in Data Science Platforms. Updated: March 2024.
765,234 professionals have used our research since 2012.