Top Rated H2O Alternatives
This is a cross-platform, integrated framework for ML that is continuing to evolve with topics important to data scientists. Review collected by and hosted on G2.com.
Developing support for a wide range of NLP preprocessing Review collected by and hosted on G2.com.
23 out of 24 Total Reviews for H2O
Overall Review Sentiment for H2O
Log in to view review sentiment.
Excellent support for commercial product Driverless AI. Rapid iteration. Performance is generally better than one can be achieved in code. Review collected by and hosted on G2.com.
Actually nothing. The combination of proprietary and open source tools, Driverless AI and H2O, provide tools across a full range of use cases. Review collected by and hosted on G2.com.
The web front end known as flow is really easy to use. It can be use to quickly create machine learning models. Review collected by and hosted on G2.com.
The complex machine learning model overfit the data. This is especially true when the data set is small. Review collected by and hosted on G2.com.
They developed top-quality open source tools, including the H2O-3 and AutoML families. I do not have a license for their Driverless AI, but my experience with it through tutorials and other demos has been superb. I should mention that their efforts to develop frameworks for ML interpretability are spot on, and their learning center is shaping up as a valuable resource to the community in general. The interfaces with R and Python enable a smooth transition of pre-existing workflows into the H2O framework. Review collected by and hosted on G2.com.
Somewhat cryptic debugging msgs in H2O-3. They support specific packages for manipulating data (data.table in R, datataable in Python) for the sake of speed and big data maneuverability, although many users may find this limiting. Driverless AI may not be affordable to the small fish in the pond. Review collected by and hosted on G2.com.
Easy to use with good UI design and automated ML function. Driverless AI has strong capability on the auto feature engineering and system visualization. The auto feature engineering has supported different machine learning algorithm (Random Forest, Decision Tree, Neural Network, Deep Learning, etc.) and feature parameter tuning (accuracy, time, system computing etc.) The system also helps user to reduce time and efforts for hyparemeter tuning and compare the model with different settings. This will optimize the process and provide the most efficient model for prediction in classification or regression domain. Besides, Driverless AI also has good UI design and visualization. The UI also supports end user to quickly import data, visualize data in different categories, as well as check on the system running and performance during the Auto ML process. The end user could also observe experiment summary and accuracy matrix, as well as model comparison in term of accuracy.
In addition, Driverless AI also supports the AI Interpretation to explain on the model and performance. This function is very helpful to end user for understanding the Machine Learning blackbox, as well as management team for decision making based on extensive information. Review collected by and hosted on G2.com.
It is great if Driverless AI could support deployment for edge computing, which is common in IoT world. The edge computing will require efficient computing and good accuracy with AutoML algorithm. This will help much the customer for deployment. Review collected by and hosted on G2.com.
AutoML is a great product. They have other ones, but AutoML is the most impressive. Review collected by and hosted on G2.com.
Nothing, maybe the integration with Spark could be improved but only in little details. Review collected by and hosted on G2.com.
h2o offers a well validated, fully automated, rigorous machine learning pipeline including state of the art model interpretation allowing for prediction and inferences. Review collected by and hosted on G2.com.
i have nothing dislike about h2o's products. Review collected by and hosted on G2.com.
The ability to try out multiple models with a few lines of the code is the greatest benefit Review collected by and hosted on G2.com.
H20 does a good job in abstraction of underlying transformation and tuning steps, which could be a bit challenging Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
The best part of H2O.ai is its ease of use and seamless UI. Review collected by and hosted on G2.com.
One downside of H2O.ai is, as with many services, its bugs which do not return human-readable debugging statements. Review collected by and hosted on G2.com.
