Compare Darwin vs. IBM Watson Studio

Darwin is ranked 9th in Data Science Platforms with 8 reviews while IBM Watson Studio is ranked 10th in Data Science Platforms with 7 reviews. Darwin is rated 8.4, while IBM Watson Studio is rated 8.4. 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 "Machine learning that can be applicable for other data sets without having to carry out the process all over again". Darwin is most compared with and RapidMiner, whereas IBM Watson Studio is most compared with IBM SPSS Modeler, Microsoft Azure Machine Learning Studio and Amazon SageMaker. See our Darwin vs. IBM Watson Studio report.
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Most Helpful Review
Find out what your peers are saying about Darwin vs. IBM Watson Studio and other solutions. Updated: March 2020.
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Quotes From Members

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.

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It has a lot of data connectors, which is extremely helpful.IBM Watson Studio consistently automates across channels.The system's ability to take a look at data, segment it and then use that data very differently.The scalability of IBM Watson Studio is great.The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video.The solution is very easy to use.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.

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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.

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The initial setup was complex.Some of the solutions are really good solutions but they can be a little too costly for many.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.So a better user interface could be very helpfulMore features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier.

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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.

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Compared 55% of the time.
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Also Known As
Watson Studio, IBM Data Science Experience, Data Science Experience, DSx

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.

IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

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Sample Customers
Information Not Available
GroupM, Accenture, Fifth Third Bank
Top Industries
Comms Service Provider64%
Financial Services Firm10%
Software R&D Company4%
Software R&D Company40%
Comms Service Provider13%
Media Company6%
Find out what your peers are saying about Darwin vs. IBM Watson Studio and other solutions. Updated: March 2020.
405,734 professionals have used our research since 2012.
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