SAS Enterprise Miner and H2O.ai compete in the analytics and machine learning space. Based on the assessments, H2O.ai seems to hold an advantage due to its modern AI capabilities and flexibility in deployment and pricing.
Features: SAS Enterprise Miner has advanced analytics tools, high performance, and scalability for large enterprises. Its suite includes decision tree creation, data management and analytics, and integration capabilities. H2O.ai provides machine learning automation, deep and adaptive learning processes, and ease of use with features like AutoML and support for Jupyter Notebooks.
Room for Improvement: SAS Enterprise Miner could enhance its user interface for simplicity and improve flexibility in integration. Increasing user autonomy and reducing reliance on dedicated infrastructure would be beneficial. H2O.ai could expand its reporting capabilities, improve initial setup speed, and offer better visualization options for ease of understanding model performance.
Ease of Deployment and Customer Service: SAS Enterprise Miner requires dedicated infrastructure and offers thorough training, ensuring a complete deployment process. It is known for extensive enterprise-level support. H2O.ai supports flexible deployment on various platforms, including cloud, offering faster installations with agile-focused customer service, appealing to technologically advanced environments.
Pricing and ROI: SAS Enterprise Miner involves a substantial initial investment suitable for long-term ROI in large enterprises. H2O.ai offers a cost-effective pricing model with a faster ROI potential, thanks to lower setup costs and efficiency in AI projects. It is attractive for its agility and effectiveness in achieving outcomes.
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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