We performed a comparison between Darwin and Databricks 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."
"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 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."
"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."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"The setup is quite easy."
"Automation with Databricks is very easy when using the API."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"It is a cost-effective solution."
"Databricks has helped us have a good presence in data."
"The integration with Python and the notebooks really helps."
"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."
"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."
"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 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 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's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"The integration and query capabilities can be improved."
"It would be great if Databricks could integrate all the cloud platforms."
"Databricks' technical support takes a while to respond and could be improved."
"The tool should improve its integration with other products."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"Implementation of Databricks is still very code heavy."
Earn 20 points
Darwin is ranked 27th in Data Science Platforms while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Darwin is rated 8.0, while Databricks 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 Databricks writes "A nice interface with good features for turning off clusters to save on computing". Darwin is most compared with IBM Watson Studio and Microsoft Azure Machine Learning Studio, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio.
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