We performed a comparison between Darwin and IBM Watson Studio based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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."
"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."
"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."
"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"It is a very stable and reliable solution."
"The solution is very easy to use."
"Stability-wise, it is a great tool."
"IBM Watson Studio consistently automates across channels."
"It is a stable, reliable product."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"Watson Studio is very stable."
"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 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."
"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."
"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."
"So a better user interface could be very helpful"
"I think maybe the support is an area where it lacks."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"The decision making in their decision making feature is less good than other options."
"The solution's interface is very slow at times."
"We would like to see it more web-based with more functionality."
"I want IBM's technical support team to provide more specific answers to queries."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. Darwin is rated 8.0, while IBM Watson 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 IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". Darwin is most compared with Databricks and Microsoft Azure Machine Learning Studio, whereas IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI, Google Vertex AI and Amazon Comprehend.
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