We performed a comparison between Darwin and Dataiku Data Science Studio based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"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."
"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."
"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."
"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."
"The most valuable feature is the set of visual data preparation tools."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"Data Science Studio's data science model is very useful."
"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."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The solution is quite stable."
"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."
"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."
"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."
"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."
"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."
"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."
"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 ability to have charts right from the explorer would be an improvement."
"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."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while Dataiku Data Science Studio is ranked 6th in Data Science Platforms with 6 reviews. Darwin is rated 8.0, while Dataiku Data Science Studio 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 Dataiku Data Science Studio writes "The model is very useful". Darwin is most compared with IBM Watson Studio, Databricks and Microsoft Azure Machine Learning Studio, whereas Dataiku Data Science Studio is most compared with Databricks, Alteryx, KNIME, Microsoft Azure Machine Learning Studio and RapidMiner.
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