H2O.ai vs RapidMiner comparison

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H2O.ai Logo
2,121 views|1,487 comparisons
RapidMiner Logo
5,712 views|4,582 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between H2O.ai and RapidMiner based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: March 2024).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"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.""AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.""The ease of use in connecting to our cluster machines.""Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O.""It is helpful, intuitive, and easy to use. The learning curve is not too steep.""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."

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"The most valuable feature is what the product sets out to do, which is extracting information and data.""The most valuable features are the Binary classification and Auto Model.""The best part of RapidMiner is efficiency.""It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive.""The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically.""The data science, collaboration, and IDN are very, very strong.""I've been using a lot of components from the Strategic Extension and Python Extension.""The documentation for this solution is very good, where each operator is explained with how to use it."

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Cons
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time.""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.""The model management features could be improved.""It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.""Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.""I would like to see more features related to deployment.""It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

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"The price of this solution should be improved.""I would like to see all users have access to all of the deep learning models, and that they can be used easily.""RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models.""In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner.""The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade.""In the Mexican or Latin American market, it's kind of pricey.""It would be helpful to have some tutorials on communicating with Python.""The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."

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Pricing and Cost Advice
  • "We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."
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  • "I used an educational license for this solution, which is available free of charge."
  • "Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year."
  • "The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."
  • "For the university, the cost of the solution is free for the students and teachers."
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    Top Answer:I've been using a lot of components from the Strategic Extension and Python Extension.
    Top Answer:I would like to see more integration capabilities.
    Ranking
    19th
    Views
    2,121
    Comparisons
    1,487
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    10th
    Views
    5,712
    Comparisons
    4,582
    Reviews
    4
    Average Words per Review
    344
    Rating
    8.3
    Comparisons
    KNIME logo
    Compared 47% of the time.
    Alteryx logo
    Compared 12% of the time.
    Tableau logo
    Compared 7% of the time.
    Anaconda logo
    Compared 1% of the time.
    Learn More
    Overview

    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.

    RapidMiner's unified data science platform accelerates the building of complete analytical workflows - from data prep to machine learning to model validation to deployment - in a single environment, improving efficiency and shortening the time to value for data science projects.

    Sample Customers
    poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
    PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company10%
    Manufacturing Company8%
    Insurance Company6%
    REVIEWERS
    University46%
    Energy/Utilities Company8%
    Educational Organization8%
    Engineering Company8%
    VISITORS READING REVIEWS
    University11%
    Computer Software Company11%
    Educational Organization10%
    Manufacturing Company9%
    Company Size
    REVIEWERS
    Small Business22%
    Midsize Enterprise22%
    Large Enterprise56%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    REVIEWERS
    Small Business50%
    Midsize Enterprise20%
    Large Enterprise30%
    VISITORS READING REVIEWS
    Small Business20%
    Midsize Enterprise14%
    Large Enterprise67%
    Buyer's Guide
    Data Science Platforms
    March 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    H2O.ai is ranked 19th in Data Science Platforms while RapidMiner is ranked 10th in Data Science Platforms with 19 reviews. H2O.ai is rated 7.6, while RapidMiner is rated 8.6. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of RapidMiner writes "Easy to use, robust, and simple to deploy". H2O.ai is most compared with Amazon SageMaker, Databricks, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and IBM Watson Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Anaconda.

    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.