Darwin Reviews

Name: Darwin
Brand: SparkCognition
Rating: 4.0 (8 reviews)

4.0 out of 5

8 reviews
100% willing to recommend

What is Darwin?

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.

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Darwin is the #27 ranked solution in top Data Science Platforms. PeerSpot users give Darwin an average rating of 8.0 out of 10. Darwin is most commonly compared to Databricks: Darwin vs Databricks. Darwin is popular among the large enterprise segment, accounting for 71% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 25% of all views.

Helped 816,406 peers since 2012

Featured reviews

AaronCooke

Founder at Helio Summit

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

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NataliaCueto

Head of Technology at CapitalTech

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 with it to get the data sets ready for Darwin. However, once they are in Darwin, I believe we will not have any problems and will have very good results, just as we have had for the risk portfolio management. We are trying to aim it for a more specific group of clients to target them more specifically. Right now, we have been using Darwin with clients that we don't want. That's how we have been reducing our delinquency index. Darwin is helping us identify clients that we need to close a relationship with, but we need it now to tell us the clients where we should be aiming to give them new products, new opportunities, or go to the market and reach new clients. The dashboards and displaying of the data needs improvement. Currently, only IT and business intelligence people are using the results we get from Darwin, but less sophisticated areas in technology could also benefit from it if we had more user-friendly dashboards. People get scared, and they think, "We will need to run something in Python," which is not the case. We could use more user-friendly dashboards so everyone could use them. However, they have already let me know that Darwin is already working on the dashboard implementation so our commercial areas can have access to the data in a more user-friendly way. This is great because it is a very important area of opportunity. We want to be able to test different updates. E.g., we've been waiting for the user-friendly dashboards since August. We really want to start working with that but don't know when it will be released. The people at SparkCognition told me that as soon as they were ready that they would contact us, so we could have a workshop for this. However, they haven't contacted us for this yet.

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reviewer1244550

Artificial Intelligence Engineer at Bosch

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. Where it's good, for example, is if we have, say, a date column with different dates and maybe that data is not so valuable for the model because the difference in the dates is not significant. Darwin will find that kind of thing. But you definitely can't give Darwin a dirty dataset and then generate a really nice model. So we have to do extra analysis of the data. Cleaning the data always consumes a lot of time. Also, Darwin can generate new data, but I didn't find that to be very valuable. Darwin could improve generating new data and that would be an important improvement.

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Darwin mindshare

As of November 2024, the mindshare of Darwin in the Data Science Platforms category stands at 0.3%, up from 0.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

Key learnings from peers

Valuable Features

"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."
"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."

Room for Improvement

"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's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

Pricing

"I believe our cost is $1,000 per month."
"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."
"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."

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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