We performed a comparison between Darwin and KNIME based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"The thing that I find most valuable is the ability to clean the data."
"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 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."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"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've never had any problems with stability."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"From a user-friendliness perspective, it's a great tool."
"There are a lot of connectors available in KNIME."
"Clear view of the data at every step of ETL process enables changing the flow as needed."
"It's a huge tool with machine learning features as well."
"Since KNIME is a no-code platform, it is easy to work with."
"It is very fast to develop solutions."
"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."
"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."
"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."
"The analyze function takes a lot of time."
"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."
"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 ability to handle large amounts of data and performance in processing need to be improved."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"There should be better documentation and the steps should be easier."
"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."
"KNIME's documentation is not strong."
"If they had a more structured training model it would be very helpful."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Darwin is rated 8.0, while KNIME 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 KNIME writes "A low-code platform that reduces data mining time by linking script". Darwin is most compared with IBM Watson Studio, Databricks and Microsoft Azure Machine Learning Studio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Dataiku Data Science Studio.
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