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523 views|127 comparisons
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7,596 views|5,013 comparisons
Most Helpful Review
Find out what your peers are saying about Darwin vs. H2O.ai and other solutions. Updated: November 2020.
447,228 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"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 find it quite simple to use. Once you are trained on the model, you can use it anyway you want.""The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.""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.""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.""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.""In terms of streamlining a lot of the low-level data science work, it does a few things there."

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"It is helpful, intuitive, and easy to use. The learning curve is not too steep.""The ease of use in connecting to our cluster machines.""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.""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."

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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 analyze function takes a lot of time.""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.""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.""There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.""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.""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."

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"The model management features could be improved.""I would like to see more features related to deployment.""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.""On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

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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."

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"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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Questions from the Community
Top Answer: 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… more »
Top Answer: Darwin has a great value statement. Customers always want it to be a little bit cheaper, but in the context of otherwise having to hire data scientists the pricing is very cost effective. There are no… more »
Top Answer: There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of… more »
Top Answer: 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.
Top Answer: On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem. I would like to see better integration with Python… more »
Top Answer: I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them. There are various… more »
Ranking
8th
Views
523
Comparisons
127
Reviews
7
Average Words per Review
1,581
Avg. Rating
8.4
12th
Views
7,596
Comparisons
5,013
Reviews
5
Average Words per Review
350
Avg. Rating
7.6
Popular Comparisons
Compared 28% of the time.
Compared 19% of the time.
Compared 15% of the time.
Compared 1% of the time.
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SparkCognition
H2O.ai
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.

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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Sample Customers
Hunt Oil, Hitachi High-Tech Solutionspoder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Top Industries
VISITORS READING REVIEWS
Comms Service Provider54%
Financial Services Firm12%
Computer Software Company8%
Retailer5%
VISITORS READING REVIEWS
Computer Software Company33%
Comms Service Provider14%
Media Company7%
Insurance Company7%
Company Size
REVIEWERS
Small Business75%
Large Enterprise25%
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
Find out what your peers are saying about Darwin vs. H2O.ai and other solutions. Updated: November 2020.
447,228 professionals have used our research since 2012.
Darwin is ranked 8th in Data Science Platforms with 8 reviews while H2O.ai is ranked 12th in Data Science Platforms with 5 reviews. Darwin is rated 8.4, while H2O.ai is rated 7.6. 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 H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". Darwin is most compared with RapidMiner, whereas H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and SAS Visual Analytics. See our Darwin vs. H2O.ai report.

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