Darwin Overview

Darwin is the #9 ranked solution in our list of top Data Science Platforms. It is most often compared to H2O.ai: Darwin vs H2O.ai

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.

Darwin Buyer's Guide

Download the Darwin Buyer's Guide including reviews and more. Updated: June 2021

Darwin Customers

Hunt Oil, Hitachi High-Tech Solutions

Darwin Video

Pricing Advice

What users are saying about Darwin pricing:
  • "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."
  • "I believe our cost is $1,000 per month."
  • "As far as I understand, my company is not paying anything to use the product."

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AaronCooke
Founder at Helio Summit
Real User
Top 20
Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

What is our primary use case?

I provide product management and SME services to oil companies as a consulting service. My company has partnered with SparkCognition to bundle its products into a package of services that I provide to my customers. For the most part, when I'm working with SparkCognition, and Darwin in particular, I'm working with it on behalf of one of my customers. We do different engagements. We've done PoC projects with customers with versions 1.4 and onward. The biggest use case we've seen is for automatic classification of data streaming in from oil and gas operations, whether exploration or production… more »

Pros and Cons

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

What other advice do I have?

Machine learning is definitely not pixie dust. Often people fall into the trap of thinking "Oh, I just throw in some AI and it's going to magically make my data better." It's certainly not going to do that. But where it does have very specific applicability to problems is where you understand what it's good at and what it's not good at. I've worked with so many Fortune 500 companies in the oil industry and they can't keep data scientists around long enough to actually finish a project and solve a problem and then reintegrate it into their system. Darwin is the perfect tool to solve this issue…
NataliaCueto
Head of Technology at CapitalTech
Real User
Helps us evaluate all our processes in a faster way

What is our primary use case?

We have been using it for our risk management portfolio. We are a lending institution. We give credit to small and medium enterprises. We've been using it mainly for client segmentation and the probability of delinquency in the loans that we get. I am using the latest version.

Pros and Cons

  • "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 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."

What other advice do I have?

Do not be intimidated by the apparent complexity of it because it is more user-friendly than you think. It makes AI easy. Start testing it because it's very trial and error. I really do believe people need to have this type of mentality to start using tools like Darwin. Don't be afraid of retesting it. We are using the automated AI model building because we want the AI model to be unique for each customer. We are getting all the data ready so it can be integrated into the modeling. We want to give each client a unique credit model to be automated through the AI. We don't have this currently…
Learn what your peers think about Darwin. Get advice and tips from experienced pros sharing their opinions. Updated: June 2021.
512,221 professionals have used our research since 2012.
JuliaJenal
Junior Data Scientist at a tech services company with 51-200 employees
Real User
Makes machine learning a lot more accessible, but there were some stability issues

What is our primary use case?

I was trying to see if Darwin was going to be useful for the company and if it was useful for the project that I was working on. I was working with it, testing it, seeing how it worked, seeing how accessible it was, and if it would be something that would be viable for us to use. We were hoping to use it on a machine-learning project, to categorize words based on their likeness to each other. I had to find a way to translate that, and encode it, into something that Darwin could actually read.

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

Go into it with the mindset of playing around with it to see how it can work with different tweaks. You would probably want to start with one of their use cases that you know is going to work properly. My project was weird and didn't really want to work properly with any machine learning, whether I put it in or it was Darwin. But when I used their use cases, it worked way better. So start with their use cases, play around with it, and really get familiar with it. I definitely have the millennial mindset of, "Here's a new piece of technology. I'm going to play with it and see how it works and…
MV
Business Intelligence Director at a financial services firm with 51-200 employees
Real User
Helps us reduce the percentage of high-risk clients we work with

What is our primary use case?

We are using it in two ways. One is by analyzing our current clients to create more business by deciding if we can offer them new products or if there is a risk of their leaving us or stopping use of our credit lines. The second side is to prevent the risk of default. Our credit clients, because of the economic situation or internal decisions of the company, can go into default and stop paying their credit lines. We use it to prevent that risk. If we see a deterioration in a client, we can decide to stop lending money to the client and prevent risk in that way. So on the one side it's to… more »

Pros and Cons

  • "The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
  • "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."

What other advice do I have?

One of the most important things we learned, and that we also recommend to other companies, is to have a data link; to have all their data ready. Without data you cannot use Darwin. You really need the data to start using it and to take advantage of Darwin. You also need people who understand data science. They can help you understand how to use Darwin and to interpret the results that it gives you. Right now we are not measuring the accuracy of the models. We are using it to give some insight and some answers. We're on our way toward that.
EC
Manager, Business Data Analytics at CapitalTech
Real User
Helps us transform data into knowledge faster by selecting the best algorithms for us

What is our primary use case?

The primary use case is to predict the default on payments by clients.

Pros and Cons

  • "The thing that I find most valuable is the ability to clean the data."
  • "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."

What other advice do I have?

You need to have good data sets to get good results. Before Darwin, you need to work on your data sets to have the correct data sets to make the correct models. Darwin is a solid solution, but the main advice that I have is that if you don't have the data, you can get Darwin but you're not going to get the results you want. The biggest lesson I have learned from using Darwin is that it makes things faster. We can test faster, not just one at a time. We speak with the team at SparkCognition and they help us to improve our ideas around the use cases that we can apply. That is another important…
MN
Artificial Intelligence Engineer at a manufacturing company with 10,001+ employees
Real User
Produces better models than we can produce ourselves but requires extra work to clean the data set

What is our primary use case?

We use it for analyzing data and creating models. We extract information from the database and then see if Darwin can share information with us about what would be nice components for the model. Then we use Darwin to make a model. We clean the data and pass it through to Darwin and Darwin generates a best model. From Darwin, we get parameters, important features, and predictions. We don't have the entire Darwin solution. We just have the core. We are taking the information about the parameters of the model and then we generate the model again with our own tools. Darwin doesn't give us the… more »

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

My advice is to do extra cleaning of your data. Darwin is good when it has a really nice, clean dataset to generate a model, but you need to work at it to make sure you have that kind of dataset. On our team there are 25 people but there are just two of us using Darwin, my partner and me. He is a data scientist and I am an artificial intelligence engineer. We are using Darwin for the development phase, but we aren't using it for production. It's a fast tool for development. Within our group in the company, we develop solutions. We try to analyze the possibilities for doing so. We need the data…
WaqarChaudhry
Consultant at a consultancy with 10,001+ employees
Reseller
Doesn't provide the functionality that an analyst would need and getting up and running was difficult

What is our primary use case?

The PoC we did was for the oil and gas field mostly, as well as the aerospace field, to optimize supply chains. We wanted to see what level of information we could gather from using this tool and how it would help us. We were looking to become a reseller for Darwin and to provide services through it to our clients. We wanted to pitch it to our clients, but our PoC indicated it was not feasible.

Pros and Cons

  • "In terms of streamlining a lot of the low-level data science work, it does a few things there."
  • "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."

What other advice do I have?

The biggest lesson I learned from using Darwin, honestly, was that they should interface with their clients much quicker and much more easily. They should make that process seamless to make sure clients are up and running ASAP so they can get their feet wet instead of wasting about a month of work. We don't have any plans to use it right now but we're open to using in the future. We're telling them this stuff because we want them to improve this product because we did see value in it. We did see the idea behind it, but the execution was not done very well, especially when it comes to tools to…
TalhaKhwaja
Software Engineer (ML/CompVision) at a tech vendor with 51-200 employees
Real User
It has good accuracy for solving complex problems

What is our primary use case?

I have been working on data analytics using Darwin. I have been working more on the data generation part. There were some problems where they wanted us to generate some synthetic data, and I was working on that part. As for the usage of Darwin, somebody else does that, but I also am getting familiar it. We were using the last version before 2.0 was released.

Pros and Cons

  • "I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
  • "The analyze function takes a lot of time."

What other advice do I have?

Once Darwin went down, then the product went down as well. This was a small issue. I would rate Darwin as an eight or nine out of 10, as a nontechnical person. I would prefer a tool with more control. A more experienced user would probably rate the product as a six out of 10.