Darwin vs Domino Data Science Platform comparison

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Executive Summary

We performed a comparison between Darwin and Domino Data Science Platform 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).
765,234 professionals have used our research since 2012.
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 feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate.""The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.""I find it quite simple to use. Once you are trained on the model, you can use it anyway you want.""The thing that I find most valuable is the ability to clean the data.""Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision.""I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable.""In terms of streamlining a lot of the low-level data science work, it does a few things there.""The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

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"The scalability of the solution is good; I'd rate it four out of five."

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Cons
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin.""The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin.""The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition.""There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.""There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets.""An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data.""Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model.""The analyze function takes a lot of time."

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"The predictive analysis feature needs improvement."

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Pricing and Cost Advice
  • "The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."
  • "In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."
  • "As far as I understand, my company is not paying anything to use the product."
  • "I believe our cost is $1,000 per month."
  • More Darwin Pricing and Cost Advice →

    Information Not Available
    Ranking
    27th
    Views
    507
    Comparisons
    260
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    17th
    Views
    2,768
    Comparisons
    2,376
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    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.
    765,234 professionals have used our research since 2012.
    Comparisons
    Also Known As
    Domino Data Lab Platform
    Learn More
    Overview

    SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

    Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

    Sample Customers
    Hunt Oil, Hitachi High-Tech Solutions
    Allstate, Tesla, Dell, Moody's Analytics, SurveyMonkey, Eventbrite, Carnival
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company20%
    Financial Services Firm13%
    Government11%
    Real Estate/Law Firm11%
    VISITORS READING REVIEWS
    Financial Services Firm28%
    Insurance Company11%
    Manufacturing Company10%
    Computer Software Company7%
    Company Size
    REVIEWERS
    Small Business75%
    Large Enterprise25%
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise10%
    Large Enterprise68%
    VISITORS READING REVIEWS
    Small Business8%
    Midsize Enterprise7%
    Large Enterprise84%
    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.
    765,234 professionals have used our research since 2012.

    Darwin is ranked 27th in Data Science Platforms while Domino Data Science Platform is ranked 17th in Data Science Platforms. Darwin is rated 8.4, while Domino Data Science Platform is rated 7.0. The top reviewer of Darwin writes "Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows". On the other hand, the top reviewer of Domino Data Science Platform writes "Good scalability and stability but the predictive analysis feature needs improvement". Darwin is most compared with Microsoft Azure Machine Learning Studio, Databricks and IBM Watson Studio, whereas Domino Data Science Platform is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio 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.