Compare Darwin vs. RapidMiner

Darwin is ranked 8th in Data Science Platforms with 8 reviews while RapidMiner is ranked 5th in Data Science Platforms with 8 reviews. Darwin is rated 8.4, while RapidMiner is rated 8.2. 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 RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". Darwin is most compared with H2O.ai and RapidMiner, whereas RapidMiner is most compared with KNIME, Alteryx and H2O.ai. See our Darwin vs. RapidMiner report.
Cancel
You must select at least 2 products to compare!
Darwin Logo
369 views|71 comparisons
RapidMiner Logo
9,255 views|7,676 comparisons
Most Helpful Review
Find out what your peers are saying about Darwin vs. RapidMiner and other solutions. Updated: March 2020.
408,459 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 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.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.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.The thing that I find most valuable is the ability to clean the data.

Read more »

The best part of RapidMiner is efficiency.Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations.The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS.The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model.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.RapidMiner is very easy to use.The documentation for this solution is very good, where each operator is explained with how to use it.

Read more »

Cons
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.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.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.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.

Read more »

I think that they should make deep learning models easier.The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive.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.A great product but confusing in some way with regard to the user interface and integration with other tools.RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models.I would like to see all users have access to all of the deep learning models, and that they can be used easily.The price of this solution should be improved.

Read more »

Pricing and Cost Advice
I believe our cost is $1,000 per month.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.

Read more »

The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license.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.I used an educational license for this solution, which is available free of charge.

Read more »

report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
408,459 professionals have used our research since 2012.
Ranking
8th
Views
369
Comparisons
71
Reviews
7
Average Words per Review
1,581
Avg. Rating
8.4
5th
Views
9,255
Comparisons
7,676
Reviews
8
Average Words per Review
629
Avg. Rating
8.3
Top Comparisons
Compared 50% of the time.
Compared 50% of the time.
Compared 35% of the time.
Compared 21% of the time.
Compared 5% of the time.
Learn
SparkCognition
RapidMiner
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.

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.

Offer
Learn more about Darwin
Learn more about RapidMiner
Sample Customers
Information Not Available
PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
Top Industries
VISITORS READING REVIEWS
Comms Service Provider64%
Financial Services Firm10%
Retailer6%
Software R&D Company4%
VISITORS READING REVIEWS
Software R&D Company21%
University13%
Comms Service Provider13%
Manufacturing Company8%
Company Size
REVIEWERS
Small Business75%
Large Enterprise25%
REVIEWERS
Small Business55%
Midsize Enterprise9%
Large Enterprise36%
VISITORS READING REVIEWS
Small Business30%
Midsize Enterprise2%
Large Enterprise69%
Find out what your peers are saying about Darwin vs. RapidMiner and other solutions. Updated: March 2020.
408,459 professionals have used our research since 2012.
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.