We performed a comparison between Darwin and IBM SPSS Modeler 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."
"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."
"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."
"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."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"Automated modelling, classification, or clustering are very useful."
"Compared to other tools, the product works much easier to analyze data without coding."
"We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want."
"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"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."
"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 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."
"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."
"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."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"The standard package (personal) is not supported for database connection."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"Unstructured data is not appropriate for SPSS Modeler."
"The forecasting could be a bit easier."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Darwin is rated 8.0, while IBM SPSS Modeler is rated 8.0. 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 IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". Darwin is most compared with IBM Watson Studio, Databricks and Microsoft Azure Machine Learning Studio, whereas IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, Alteryx and IBM SPSS Statistics.
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