Most Helpful Review
Find out what your peers are saying about Darwin vs. Dataiku Data Science Studio and other solutions. Updated: March 2020.
406,607 professionals have used our research since 2012.
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
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.
I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.
The most valuable feature is the set of visual data preparation tools.
The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.
Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.
Cloud-based process run helps in not keeping the systems on while processes are running.
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.
I find that it is a little slow during use. It takes more time than I would expect for operations to complete.
In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.
The ability to have charts right from the explorer would be an improvement.
Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.
Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).
There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.
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.
The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything.
out of 33 in Data Science Platforms
Average Words per Review
out of 33 in Data Science Platforms
Average Words per Review
Compared 55% of the time.
Compared 45% of the time.
Compared 36% of the time.
Compared 13% of the time.
Compared 11% of the time.
Also Known As
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.
Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.
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Software R&D Company4%
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Comms Service Provider12%
Financial Services Firm11%