We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
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.
SAS Visual Data Mining and Machine Learning combines data wrangling, data exploration, visualization, feature engineering, and modern statistical, data mining and machine learning techniques all in a single, scalable in-memory processing environment. This provides faster, more accurate answers to complex business problems, increased deployment flexibility and one easy-to-administer and fluid IT environment.
H2O.ai is ranked 14th in Data Science Platforms with 1 review while SAS Visual Data Mining and Machine Learning is ranked 25th in Data Science Platforms. H2O.ai is rated 7.0, while SAS Visual Data Mining and Machine Learning is rated 0.0. The top reviewer of H2O.ai writes "Good collaboration functionality, but better integration with Python for data science is needed". On the other hand, H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and Alteryx, whereas SAS Visual Data Mining and Machine Learning is most compared with SAS Enterprise Miner, SAS Visual Analytics, Microsoft Azure Machine Learning Studio, WPS Analytics and Alteryx.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.