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Darwin OverviewUNIXBusinessApplication

Darwin is #9 ranked solution in top Data Science Platforms. IT Central Station users give Darwin an average rating of 8 out of 10. Darwin is most commonly compared to H2O.ai:Darwin vs H2O.ai. The top industry researching this solution are professionals from a comms service provider, accounting for 41% of all views.
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: November 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."

Darwin Reviews

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AaronCooke
Founder at Helio Summit
Real User
Top 5
Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

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 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. We see the customers using it to quickly and intelligently classify the data. Traditionally, the way that would be done is through a very complicated branching code which is difficult to troubleshoot, or by having it manually done with SMEs or people in the office who know how to interpret the data and then classify it, for analytics.

The customers have looked at using machine learning for that, but they run into challenges — and this is really what Darwin is all about. Typically there is an SME who can look at the data and properly classify it or identify problems, but taking what he knows and what he does instinctively and communicating it to a data scientist who could build a model for that is a very difficult process. Additionally, data scientists are in very high demand, so they're expensive. 

SMEs can look at data and quickly make interpretations. They've probably been looking at the data for 10 or 15 years. So it's not a matter of just, "Oh, we can plunk this SME beside a data scientist and in a couple of months they can turn out a model that does this." First, SMEs don't have time to be pulled out of their normal workload to educate the data scientists. And second, even if they do that you end up with something very rigid 

With Darwin, customers can empower the SMEs to build the models themselves without having to go through the process of educating the data scientists, who may leave next week for a better paying job.

Most of the projects that we've done, PoCs, are typically done in the cloud, for ease of use. Because we work in the oil and gas space, public cloud is the preferred option in the U.S., with the simplified administration and a little bit lower cost. Overseas, the customers we've talked to have noted there are laws and restrictions that require their stuff to be on-premise. We've talked to potential customers about it, but we haven't actually done an on-premise project so far.

How has it helped my organization?

The automated AI model-building reduces the time that projects take. Before I started working with SparkCognition, I worked on several projects where it took months, and in some cases years — complex problems — for data scientists to even pick a machine-learning model to use. They might settle on a methodology such as random forest after quite a bit of analysis. Whenever a model is completed, it is a powerful and unique solution that can't be done with traditional programming, but it's almost impossible to tune in the field. Additionally, if you're talking oil and gas, some of the sites where you need to run these, especially on the edge, are very remote. It doesn't respond the way you want. So then you have to take it back to a data scientist and have it tuned. 

Darwin lets you rerun the process with new data, with more data, with different tuning parameters. You don't have any of that back-and-forth with a physical person.

The solution has created the opportunity for machine learning to be practically implemented in places where it couldn't be implemented before. The current way that machine learning problems are implemented is with data scientists, usually as IT initiatives or R&D initiatives. Often, a company will say, "Okay, we're going to do machine learning." They have a big initiative, then hire some very expensive data scientists and they create a model that may be 10 or 15 percent better than what's out there. The challenge is that the model exists in MATLAB or Python but it's not integrated into the business systems like an ERP; or it's not integrated into their industrial control system. It ends up being a really cool PoC and it never turns into something that practically affects the business.

Darwin has opened up the places where you can do that.

The potential we see with Darwin, with the REST API and with the easy-to-approach interface, is that we can empower these SMEs to build things and interface them from day one with the existing control systems and other systems the business is using. So they're not stepping out of their traditional workflows to use machine learning. It's integrated.

As far as building models goes, versus hiring people with ML experience it is significantly faster and exponentially cheaper. You can build models, once you've done some initial training in Darwin, that would take a data scientist, with an SME, two or three months to build. With Darwin the SME can do that in a few days or less. For a lot of applications, especially in oil and gas, the savings are huge as far as practical applications of machine learning go, versus the tradition of using data scientists to build them one by one.

For our customers we have primarily looked at use cases around automatic event detection. We hadn't even tried to do that with data scientists because it just wasn't practical with the timeline and because the costs were too high. And using traditional software methods to try to solve that problem, the estimate that the customer had was that it would have taken three to four months of software development. We were able to build a model that provided effectively the same results within a week. A lot of that was just figuring out the data and data quality issues. The actual model building in Darwin took a few hours.

What is most 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. 

It also has the REST API which is used pretty extensively. It's a bit more feature-full and it is a great tool for customers who actually want to integrate ML models into their business systems, products. and workflows. This is a challenge we see with with machine-learning initiatives at a lot of companies: You hire data scientists. You give them a problem. You give them the data. You train them on what they need to do with it. And then they build a model but you can't just drop that model into your ERP. Or if you have supervisory systems, industrial systems like IoT applications, you can't just drop a model into that. Darwin and the REST API it has available abstracts all that away and makes it very easy to integrate into existing systems.

The accuracy, like anything else, is dependent on having a good data set. If you give it the right data — good, clean datasets — Darwin is as good, if not better, than anything out there. Even if, in its automated fashion, it initially returns something that may not be quite as accurate, the fact that you're able to iterate and correct the data quality issues quickly, rather than the traditional process where you work with the data scientists and you start getting results weeks or months later, enables you to iterate quickly to get to a higher level of accuracy.

Darwin's automatic assessment of the quality of those datasets does a good job. Additionally, its partner network provides industry-specific tools that integrate and work alongside Darwin, or wrap around Darwin, and provide a lot of additional capabilities. Darwin does a good job but where it doesn't, SparkCognition has a great partner network that has developed industry-specific things that solve problems that Darwin might not solve out-of-the-box.

The solution's interactive suggestions on how to address dataset issues to make the data ready for algorithmic development is interesting. It depends on the specific data set. Sometimes they're spot-on and sometimes it's a matter of the interpretation dataset. Overall, they're helpful and they definitely make the machine learning more approachable.

What needs improvement?

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.

For how long have I used the solution?

We started talking to SparkCognition in September or October of 2018, so it's been about a year.

What do I think about the stability of the solution?

The stability has definitely improved. Early on, there were some cases where we would run into the limitation on the data size but these have been resolved. But overall, we haven't had any issues with stability.

What do I think about the scalability of the solution?

The scalability is good. The way that we use it, where we build models and then deploy them to the edge, I don't think that we would run into traditional scalability challenges because of this deployment model. We build a model, tune it, and then we integrate it into a workflow or software. Once it's there, that model is outside of Darwin.

We look at every project that our customers present to see if it's a good candidate for Darwin. I definitely want to increase our use of Darwin in projects because it provides great return on investment for our customers. 

How are customer service and technical support?

Tech support is really good and so are the customer success team guys. They're a terrific team to work with. They're always quick to get our team what we need to support our customers. Sales and product support have been outstanding. 

Which solution did I use previously and why did I switch?

The route we looked at previously was that of hiring data scientists and having them build a model. That wasn't a business we wanted to be in, as far as our consulting goes. Darwin really opened up the market and let us add more value to our customers without changing the type of staff that we have.

How was the initial setup?

The initial setup is relatively straightforward and, where there are industry-specific needs, that's the value that we, Helio Summit, brings to the table. We connect the dots between a company and SparkCognition and their products. We're there to help customers get to that value really quickly.  In our model, our consultants already have experience and training with the product. We've run PoCs and we're there to solve a problem and ensure they go smoothly for our customer.

The projects we do can take from a few weeks to a month. It depends on the size of the customer and how integrated they want it. Each customer's problems are different and each one requires integration to different systems.

Other than project management best practices, we don't really have an implementation strategy. We're a consulting company and each customer engagement is unique. We're going in there and developing something to solve a customer's specific problems. Each one is pretty unique. 

On our side, it generally doesn't take more than one or two consultants on a project, depending on the amount of interfacing that needs to be done with existing systems. If we have to tie it into WITSML or an IoT system, there may be a need to have one of our developers assist with the project. But usually, one business analyst is enough in terms of people who need to get in there and actually interface with the customer.

Our goal is to deploy solutions for our customers that are are intuitive and reliable and don't require ongoing maintenance contracts. We really want to get the customer trained and using it on their own. Unless they have problems or want to do something else, we try to get to a point where the customer is self-sufficient as quickly as possible.

Our two biggest users are usually drilling engineers and geologists as well as data analysts who also use it. These will be people who run centers of excellence for their areas, for their departments, and for their companies. They will be the best SMEs that they have and they're there to advise other people throughout these companies.

What was our ROI?

Due to the nature of our business as a partner, we don't calculate an internal ROI.  For our customers, we believe they see a return of between two and three times versus staffing an internal ML team.

It provides more value because we're enabling them to do something with machine learning that would otherwise take a lot of development or data scientists.

What's my experience with pricing, setup cost, and licensing?

Darwin has a great value statement. Customers always want it to be a little bit cheaper, but in the context of otherwise having to hire data scientists the pricing is very cost effective.

There are no additional costs unless you need custom development for system integration. We haven't run into anything beyond the licensing cost. If we need their services group to look at something, the pricing is pretty standard. There haven't been any surprises.

Which other solutions did I evaluate?

We looked at some of the PowerAI stuff. We really got into it because we had a customer with a very specific problem. We started looking at what's out there and we looked at a few including PowerAI and some of the open-source stuff. That's when we came across SparkCognition at an industry event and dug into it. It turned out to be a good fit for what this customer needed and the relationship grew from there.

What stood out to me about Darwin was how approachable it was for people who aren't in data science. For the science consultants we had, it clicked. It made sense. It was easy to articulate why you would use it and how.

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; what the machine-learning industry needs at this point to expand exponentially in the oil and gas market.

That's not to take anything away from what data scientists do. Solving these very difficult technical problems needs to be done by data scientists. But there just aren't enough people to practically apply that to the hundreds of thousands of actual use cases around the world in different industries. Having AI building AI models is really the only way to go if it's going to expand beyond larger companies.

We've looked at Darwin to create data pipelines in production for models. As I said, SparkCognition has a great partner network. In particular, a partner called Cybersoft has a really interesting tool that wraps around the models that Darwin creates and lets us run them at the edge. We found this add-on that makes it a lot easier. Darwin is great but SparkCognition's partner network builds on what they have, so that it can be applied quickly to other industries. Darwin's connectors to common data repositories cover some 70 to 80 percent of the needs out there. The oil and gas industry has some very unique data structures, like WITSML and OPC that we, as a partner, can help integrate. In some cases, there are unique data structures where we have to do a little bit of development to bridge that gap or to streamline it so they can use it again without getting out of their existing toolset.

Darwin is really the only thing that I've seen that does what it does. That alone makes it a 10 out of 10. The alternatives are so different.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Google
Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor. The reviewer's company has a business relationship with this vendor other than being a customer: Partner.
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NataliaCueto
Head of Technology at CapitalTech
Real User
Top 5
Helps us evaluate all our processes in a faster way

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 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.

How has it helped my organization?

Due to the predictions that we have been able to do because of the use of Darwin, we have decreased our delinquency index. We were almost at nine percent by July. After using Darwin, we have reduced the delinquency index to five percent. Darwin is important for this metric.

We calculate the potential loss of clients. When some of our clients are going to go with competitors, they will ask for money with another bank or institution. In July, we had this number at 19 percent of our clients. We saw that they were likely to go with some of our competitors. Now, after using Darwin to narrow and segment more of our clients, we have been contacting them in a more specific way and targeting them more specifically. So, our clients loss index has gone down to 10 percent. This is also important.

I like the solution’s automatic assessment of the quality of data sets for uncovering issues like missing data points, low statistical variance, or incorrect data types because of Darwin's process capacity of filling in the data for us or even giving it double or triple checks. However, I still believe that the data cleaning process should be cleaner and better before it arrives to Darwin. We still have a lot of processes to do before we put the data sets on Darwin.

We have a process which gives us the credit score for all the credit companies in Mexico. We have to clean the data first through Darwin, so that we can use it in our modeling. Darwin has been very helpful on cleaning this data with simple queries.

We use it more for production of models. We have not had any trouble whatsoever with the data repository connection. It's consuming the data sets correctly, which is good.

It does affect the decision-making of our risk committee using the new models. They adjust the data accordingly, then this committee reviews everything each week so we can be fast enough to compensate for any new approach or findings that we have through Darwin. It is constantly optimizing the model, so once a week has worked for us.

Darwin allows us to make better decisions and more quickly.

I believe that Darwin is helping us to evaluate all our processes in a much faster way. That is why it affects our productivity for the good. It solves a lot of the time consumption. This really makes a difference for us: the time consumption. The processes, evaluation of the processes, and results may be the same, but the time it takes us to process it through Darwin rather than on traditional tools or other tools is so radically different. It is changing the way we make decisions because we can make decisions faster, and maybe, that's the most valuable thing about Darwin.

What is most valuable?

The model processing is valuable. However, what I find most valuable of all is the time. With the team, we could have maybe reached these numbers, but it would have taken double or triple the time to reach these numbers. So, with the Darwin tool, we are able to test our models constantly. We can go with the optimal way in minutes. That has been a game changer for us.

The tool is very powerful and has many benefits. The time reduction in the modeling testing is the most valuable thing at the moment. The time that Darwin saves for us to be constantly testing the model has been a game changer for us. We could have reached these numbers maybe with qualitative analysis, but it would definitely be with more time. With Darwin, we are reaching these numbers very quickly.

The solution tracks the health of models. We're also looking into the possibility of having alerts. So, when Darwin can find an optimal model better than what we currently use, it lets us know.

What needs improvement?

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.

For how long have I used the solution?

I have been using the solution for six months.

What do I think about the stability of the solution?

We find it stable. I haven't heard of any technical problems. The availability is more than 99 percent.

What do I think about the scalability of the solution?

The solution’s ability to capture complex relationships over time and the resulting accuracy of its predictions is excellent. We don't have restrictions on the amount of data or processes that we can run at the same time. The opportunities are limitless. In fact, what I will be working on is getting all new data sets with all new correlations that we want to try on the modeling, as we are seeing that the tool is even more powerful and we can get more benefits from it than the ones we are currently getting. So, we are getting ready many other data sets to test them.

Currently, only the business intelligence team and risk management team are using Darwin. But, we want to see the dashboards in the near future so we can have the implementation for the commercial area too. Because right now, the final user is not reaching the data. So, the next step is to let the final user reach the data and make better decisions with it.

We have five licenses now: Three of them are for the business intelligence unit and two of them are for the risk management unit.

How are customer service and technical support?

Whatever problems that we have had, their team has given us super fast responses and are always willing to help us with our questions.

Which solution did I use previously and why did I switch?

We did not use another data intelligence solution before Darwin. We used regular traditional statistical tools, like Data Studio and R, but not something as powerful as Darwin.

How was the initial setup?

The initial setup was straightforward. We had no problems whatsoever. They were very clear on the instructions and there were minimal technical requirements to install. 

It took us less than two hours to deploy Darwin. When we deployed the tool, we only used one person from IT operations to install everything.

What about the implementation team?

We had two workshops with the business intelligence team and technology team so the setup could be done prior to the workshop, then the workshop could be done with different models, etc.

We are actually aiming to have a new workshop.

What was our ROI?

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.

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.

What's my experience with pricing, setup cost, and licensing?

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.

Which other solutions did I evaluate?

We went with Darwin for its potential of disrupting the market with a new type of technology that could allow us to give unique customer journey experiences, which is what we were aiming for.

We looked at Oracle and others who had their business intelligence solutions along with other things. However, we didn't feel that they were disruptive enough, especially not for our markets. We really do want to do things differently. We made it clear that we wanted to go where no one else was going. SparkCognition presented a very good tool to do that, which is why we wanted to invest in their licenses and start seeing what we can take out of them.

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. We are working on it. Right now, we don't have this in production, but are working on it so we can get there.

I would rate them a nine (out of 10). I wouldn't put them as a 10 because there are still a lot of things for them to keep trying. However, so far, there are a lot of benefits that we could be taking out of it, but that is part of the learning process.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Other
Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
Learn what your peers think about Darwin. Get advice and tips from experienced pros sharing their opinions. Updated: November 2021.
553,954 professionals have used our research since 2012.
JuliaJenal
Junior Data Scientist at a tech services company with 51-200 employees
Real User
Top 10
Makes machine learning a lot more accessible, but there were some stability issues

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 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.

How has it helped my organization?

Darwin is really useful for people who don't necessarily do a lot of data science. A lot of what you're doing in Darwin, while it's a lot more efficient, you could do yourself if you had the knowledge and the time. It's definitely more efficient and it's also useful if you are someone who does not really understand how data science works, but you still want to implement some machine learning.

It gave me ideas for how I would want to implement something on my own. I wasn't going to use Darwin as my long-term solution for what I was going to do, but it gave me the ability to tweak it in different ways, and to do that a lot faster than if I was going to code it myself on Azure Notebook or AWS. I found that really useful. It made my workflow a lot more efficient.

My project, perhaps, was not the best fit for Darwin. It did not necessarily help me find the answer for that project, but it did allow me to work with different ideas of how I would want to set up the project and pursue those answers. So while it didn't hand me the answers, it gave me the sandbox to find that more correct workflow.

It saved time in two ways. One was that it made it more efficient to tweak some options and then run it. I didn't have to put in all the code myself or rewrite the code. And it also made it a lot easier to have multiple machine-learning processes run at once. Darwin has it neatly all in one place, which makes that a lot easier to use and, again, more efficient.

What is most valuable?

I really liked how there were a lot of abilities to tweak how it was going to run: How many folds you were going to use and cross-validation. 

Also, while it wasn't super-relevant to me, 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. I didn't use it too much but it looked like it worked well from the few times that I was involved in that feature. That's a great feature to have because cleaning datasets is a pain and there are often errors even after it has been cleaned. So to have another check to say, "Oh wait, there might be a problem here," is really useful.

Darwin's interactive suggestions are useful in how it could, for example, find what a more appropriate data type might be when you have it in the wrong data type. And sometimes it would tell you, "Oh, maybe you just want to drop this," especially if it was redundant or there was low variance. It's useful to see where there might be issues. I wouldn't necessarily trust it to do all of that itself. I would say it's more of a check rather than a be-all-end-all cleaning tool.

I found the interface really clean and easy to use.

What needs improvement?

There were a couple flaws when I was using it that they've probably fixed by now. For example, scroll bars were not sticking; various little things that made it feel like it was still in beta mode. 

What I found was most important was the accessibility aspect. To that end, it could have more explanatory tool tips as an option, as a setting you could turn on and off or as a roll-over where they would pop up.

There is also transparency. 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. Of course, if you're too transparent, that's overwhelming for the person who's looking at it, and it could also be an issue for SparkCognition. It's difficult to find the point of appropriate transparency so I wouldn't blame them if they're not yet at a great point with that. But they should be thinking about a way to be appropriately transparent. That would be helpful in trusting the answers that Darwin provides.

For how long have I used the solution?

I used Darwin for a few months. I haven't used it in the last few months.

What do I think about the stability of the solution?

For the most part, it performed pretty well. 

At one point, there were some issues with it functioning correctly. I brought this up to the support team and they said, "We'll work on it immediately." They were really responsive. But they made it impossible to log in the next day while they were fixing it, and they didn't tell me beforehand that they were going to do that. I said, "Okay. Next time you really need to tell me that you're going to make it impossible to log in," because I thought something else had gone wrong. I didn't know that was just part of their fixing what was wrong from the day before. I think they heard me on that comment. 

It had a couple of issues with being perfectly stable and sometimes, if I did something that it didn't like, it would still try to run it but would then crash that process, and it would be hard to tell beforehand that that was going to happen.

How are customer service and technical support?

The people at SparkCognition were actually super-helpful in helping me figure out how to use it for my project. I'm still a student and I'm still learning a lot of things. Getting some help in how to encode it and make it work was great. The amount of support that the team gave me was really great.

Whenever I had an issue, I could bring it to them and they'd be on it immediately. They were super-responsive and that was really good.

What was our ROI?

ROI is something that I am not super-qualified to answer, but the fact that it saves time, allows for more flexibility, and also helps more people get involved in machine learning, rather than just the people who have studied it, means probably has a lot of use and return. But I don't know what the actual breakdown of costs and benefits are.

Which other solutions did I evaluate?

I've never used anything else that packages everything together and does a lot of the work on its own. I used Azure Notebook and I use AWS coding in their version of Jupyter Notebook. I did use Weka back in college, which is also a machine learning software solution, but it's nowhere near as clever as Darwin. Weka is very clunky and awful to use.

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 pick it apart in my brain." That's the attitude that you have to bring to this.

Something that I learned from using Darwin was how the question of ethics intersects with how people are actually going to be creating their software. One of the things I've been doing at this company is looking at ethics and AI and their intersection. It was interesting to see how that actually plays out in the real world; how that transparency might be an issue or might not really be an issue.

In terms of the guidance that it provides towards making models operational, I would like to see a "before" and "after" if I ran the data set the same way, but changed things based on the guidance. It would help to see the before and after of how different and more accurate it becomes. I didn't do that. I didn't want a before and after check. So I don't really know how well it worked. 

It's more of a check, something to keep you from messing up your data entirely or messing up the process entirely and wasting a ton of time. I don't think it's a be-all-end-all that can totally clean your data set for you, or totally guide you as to what you need to do. I do think it's useful in that it makes working with the data a lot more accessible. And I would say that's the main thing that I liked about Darwin, is that it makes machine learning a lot more accessible to people who don't really know what they're doing.

I would be happy to use it again to see how it's changed since I last used it.

I would rate Darwin at about seven out of ten. It's not perfect. It's definitely valuable but has a ways to go.

Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
MV
Business Intelligence Director at a financial services firm with 51-200 employees
Real User
Top 20
Helps us reduce the percentage of high-risk clients we work with

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 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 create or attract more clients by identifying certain trends or certain characteristics and offering them more products. And on the other side, it's to prevent the risk of credit default.

How has it helped my organization?

Both of our use cases are valuable. On one side it creates more business, but on the other side, it prevents impact on our company's equity. We have seen positive results on both sides, but especially on the risk prevention. Still, we have seen positive results in the generation of more information for our commercial team to attract clients as well. We have managed to reduce the percentage of high-risk clients from 8.9 percent to 5 percent. That's very significant in the overall quality of our clients. That's very good news for everyone, especially our investors.

Darwin is also helping us to make models faster and increasing the efficiency of the team. Instead of trying and trying to prove various models, we get a model fast. It's making data science faster but we would like it to be even faster still.

It has helped our team to convert data into knowledge and to more deeply analyze the results.

What is most valuable?

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

What needs improvement?

We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want. This is one thing we've told the people at SparkCognition and they're working on it. In these kinds of situations, we don't use Darwin 100 percent because of that limitation.

The solution does help us towards making models operational but we are not at the place we want to be. We want models giving the answer to whether we should make a loan or not, but we are not at that point yet. It still has some limitations. These are things we have given as feedback and they're working on them.

Also, it would be great to have a solution that can organize the models. Right now, when there are a lot of models, they are disorganized. In the future, when we have more models, it will be more complicated to find the things that we are working on. It's about the user interface. You have the screen where you can look for other models but you can't organize models by name or by date.

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. But it would be great to have an API that gives us the opportunity to have a connection with our datasets or data lakes for each one, and a specific file for each model. Sometimes, you find that you have to add a new variable and you have to create a new file with that variable instead of having a connection via API to your datasets.

We have also asked SparkCognition that instead of automatic suggestions for addressing dataset issue, things should be defined by the user instead. There have been occasions where we have numerical data and Darwin has suggested using a nominal variable. We would prefer to define categories ourselves, instead of the recommendation that Darwin makes.

For how long have I used the solution?

We have been using Darwin for about six to seven months.

What do I think about the stability of the solution?

The stability of the platform has been okay. We've been using it regularly. Anytime we have a problem we call our contact and it's solved fast.

What do I think about the scalability of the solution?

We need to finish some internal changes and some internal processes so we can go to the next step and scale it. Our dream is for even our commercial team to start using Darwin — not making the models but using different applications. Our dream is to scale it to check all our possible prospects. We have work to do on our side on the scalability.

How are customer service and technical support?

Tech support has been good. When we report something or when we need help, they have responded really fast. We are usually in touch with a particular person. When we call him or tell him what our problem is, we immediately have our response.

Which solution did I use previously and why did I switch?

Our CEO started everything. He brought Darwin to us. We have also been using R and SPSS.

There are a lot of differences between each of them. In R you have to code, but you can do more things. The nice thing about Darwin is that you don't have to code. It's user-friendly. But there are some things that you cannot do with Darwin that you can do with R. It's a good solution for basic information. We are using more unsupervised models because we are using clusters. That's the reason we are using the other two solutions.

How was the initial setup?

The initial setup was complex because it was the first time we used this kind of AI tool. They visited us here in Mexico, which was good, and they prepared us. They explained what Darwin is, what we can do with it, some use cases, etc. Since then, they've been helping us and answering our questions and helping us with any issue we have. At first it might have been difficult because if it was something new, but we adapted really fast and started using it immediately.

Our deployment took about four or five weeks.

We didn't have a specific implementation plan other than just to begin using it and to give feedback to SparkCognition on any issues, recommendations, and thoughts about the product.

There were four or five people involved with us from SparkCognition at different points, people who came here or took part in video conferences. On our side, overall, there were between 12 and 15 people involved, including our BI and IT teams. But not all of them use Darwin. We have about five or six regular users. The rest got some general knowledge about Darwin and what we can do with it.

What about the implementation team?

At first, we did not work with a third-party. Right now we are working in an alliance with Xprtica. It's an arrangement that we have just started. We are ahead of them in terms of knowledge about Darwin so we're waiting for them to also get used to Darwin and to learn more about it so that they can start helping us with the different things we have in our plans.

What was our ROI?

We haven't seen a return on investment yet, but we can start calculating it regarding the clients we have detected who may fall into default. That's one of the things we want to do to determine if we've been effective or not in that prediction. And we also want to calculate what would have happened if those clients actually would have gone into default. What the impact would be on our equity.

Which other solutions did I evaluate?

We didn't test any other AI solutions.

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
EC
Manager, Business Data Analytics at CapitalTech
Real User
Top 20
Helps us transform data into knowledge faster by selecting the best algorithms for us

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 is our primary use case?

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

How has it helped my organization?

We have a lot of data sets with different kinds of mistakes. We're using Darwin to help us to fill in the blank spaces. It helps us to solve that problem and clean the data when we have blank spaces in it.

We are increasing our number of operational algorithms. We are developing algorithms to predict data for the next month, including forecasting our income using macroeconomic variables. About 80 percent of our models are operational.

We are able to develop models faster. We are a small team; there are three of us involved in business intelligence. We develop models faster because, instead taking three weeks to test a lot of algorithms to select the best one, we just upload information to Darwin and let the software help us define which is the better algorithm for that data set. We developed a model for a customer and we are on track to have a new version of that model in two months. We can do that faster because of Darwin.

Darwin enables our company to tackle more complex problems by making data science more approachable and operational. While we need people who understand what's happening in the business, the solution helps us to use our time better. We need to be more creative to have more variables that we can use for making models. We are working on creating bigger and better data sets, to increase the quality of our data sets, and on the automation of the model and the result of the model.

It has increased both efficiency and productivity. It's helping us to improve our data sets and add new variables, instead of proving and testing a model for multiple weeks. Our productivity is increasing because we are making better decisions. We are making a change in the company, becoming a data-driven company and really using our data better. For example, with our recent development for the sales team, there has been a 20 to 25 percent improvement in efficiency because we developed a solution that helps the sales team to identify when a client has enough money.

Our main goal is to transform data into knowledge and Darwin is definitely helping us to do that faster. An example is the issue of loan defaults. We have a lot of data on loans that we have already made to clients and, on some of those loans, the client defaulted in payment. We took that data and converted it into a model that helps us to predict which clients might default. We have started to cancel those loans and recover the money. Without that data, we would have a higher rate of payment default.

What is most valuable?

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

In addition, it helps us to create a model. Instead of trying things one-at-a-time, Darwin helps us to improve models and select the best one.

What needs improvement?

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. It would provide great automation because right now it takes time to download the information and then upload it to Darwin.

Another area for improvement would be if the user interface could have non-supervised models. That would be great. Right now you can only work with supervised models.

Finally, I would recommend that they work on improving the account functionality because we have had some difficulty in that area, in terms of logging in.

For how long have I used the solution?

We have been using Darwin for almost eight months.

What do I think about the stability of the solution?

In the first version, I think they got some feedback, but the stability has improved with the new version. We did have some problems when we tried to upload some data but it's getting better.

What do I think about the scalability of the solution?

The solution has scalability. The only thing that I would recommend is that it provide folders because we are using a lot of data sets. As we get more and more data sets and models, it gets a little bit complicated to find the model or the data set that you are looking for. Regarding scalability, that could be an issue in the future.

We have five people using Darwin. Three are in IT and two are members of the management team.

We don't have plans to increase usage of Darwin because our company is not that big. You need people with certain kinds of knowledge to work with Darwin. But currently, we are in the optimal position in terms of number of accounts.

How are customer service and technical support?

Technical support for Darwin is good. We have had some trouble with our accounts in terms of being able to log in, but they responded to the issue very fast.

How was the initial setup?

In the beginning, it seemed a little complex. We needed an introduction and some sessions to help us to understand what was happening. Maybe it would be easier if we just didn't ask any questions and just uploaded and ran the model. But we had a lot of questions about the results and how to compare them. That was the complex part. But the main reason we had a lot of questions was because our team, in general, has a lot of questions and wants to know more about everything.

It took us about six to eight weeks to deploy Darwin.

We already had a group working on machine learning, so that made it easier. Before Darwin, we worked with Titan and RStudio. The complex part was to take models we had already deployed in our pipeline and make them in Darwin. Our strategy was to first prove the models that we had already made, and then to start on new models. 

Now, we are aiming to have a model that will help us make the decision about whether we should make a loan or not to a given client.

What about the implementation team?

We developed things with the help of SparkCognition. We have a really good team. We understand how AI works. We didn't require any consultants.

What was our ROI?

It will take a little bit more time for us to create more models and convert more data into knowledge. I'm sure that we will have the return of investment that we aim to have. It will take some time.

Which other solutions did I evaluate?

In my studies, I work with a solution called BigML.

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 lesson.

The biggest problem for us is data sets because, sometimes, they don't pass in relation to Darwin. It's not a problem on Darwin's side, it's a big problem for us because we have a lot of unstructured data and we are working with other solutions, not Darwin, to have the data ready for algorithms.

For Darwin, as a solution, you need people who understand the business and who understand how to improve the organization with the results of the models.

Which deployment model are you using for this solution?

Private Cloud
Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
MN
Artificial Intelligence Engineer at a manufacturing company with 10,001+ employees
Real User
Top 10
Produces better models than we can produce ourselves but requires extra work to clean the data set

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 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 actual model to use, just the parameters.

We work with Darwin through a webpage and create models there and do linking analysis of the data. We are also working in the SDK version. We connect with the cloud, through the console.

How has it helped my organization?

When you start to use Darwin, you realize how the parameters improve the model. Darwin uses an algorithm that creates 10 or 15 generations and gives you a really accurate model; a more accurate model than we used to make. When we have a clean dataset, within two to three hours we have a really nice model, one that is better than we could generate in a week. A couple of hours with Darwin is like a week of work for us. That's where it provides the most value: in saving time for us. 

What is most 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.

Another feature that is really nice is that Darwin gives you a first impression of a dataset, even if the dataset is a bit dirty. Darwin can give you information about a particular column or feature where there is a lack of information. As a result, we know to do another round of manual cleaning. That's a helpful feature too.

Darwin has also increased our productivity, maybe by as much as 20 percent.

What needs 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. 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.

For how long have I used the solution?

We've been using Darwin for about three months. 

What do I think about the stability of the solution?

For us, the stability has been good. But when we were training in Darwin, it was not really clear if a model was complete or was still running. They can increase the stability in that sense. One or two times, it seemed like it was not running. It stopped. We waited a couple of minutes and then it was ready.

How are customer service and technical support?

We haven't needed to use support. We haven't had any big problems with it.

Which solution did I use previously and why did I switch?

We did not have a previous solution. I'm just an engineer. My manager came to me and said, "Now we have Darwin," and we started to use it.

How was the initial setup?

The web-based interface is really easy for setup, but the SDK is not easy, although it's also not really complex. I have a big PDF from them with a lot of information but it's not structured in a convenient way. For a first-time user, it's kind of complex. The documentation is not that clear or easy.

Our deployment of the SDK, the first time, took two or three days, although we didn't work on it the entire day. We tried to move it forward a little bit at a time. The web platform is really easy. That took a couple of hours.

What about the implementation team?

We deployed it ourselves. We have a lot of interns in the group so we developed it on our own.

What's my experience with pricing, setup cost, and licensing?

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

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 so we extract it and then generate a model. Once the model is ready we put in an API or the cloud or the web. We can then query the model with new data and create a forecast, but it depends on the solution and on the data in the production phase.

I believe we have a one-year licensing agreement. We are trying Darwin to see how it works and its benefits for us. It's hard to say if we will continue using Darwin. We are trying to determine if Darwin is a high-value tool. We need to use Darwin more. I have only been using it during about 5 or 10 percent of my time.

I would rate Darwin at seven out of 10. Darwin saves us some work, but we also have to do extra work. It doesn't do all the work for you. In the beginning, when we started to see how Darwin works, we thought that maybe, from raw, dirty data, we could generate a model really fast, but that's not true. It's good at doing some parts of the work, but you need to work and to think about the solution to your problem. You need to think about the application to generate data according to your solution. Maybe that's a good thing. If Darwin did everything, perhaps I would not be needed in the company.

Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
WaqarChaudhry
Consultant at a consultancy with 10,001+ employees
Reseller
Top 10
Doesn't provide the functionality that an analyst would need and getting up and running was difficult

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 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.

How has it helped my organization?

It's a good tool for a beginner business analyst. I don't think it's good for advanced analytics.

When I created a model, versus when Darwin created a model, my model seemed to perform better, simply because I have more knowledge about the actual business policies and problems that we have. Darwin was just giving me a beginner, data science-type assessment.

It might have improved our ability to tackle more complex problems by making data science more approachable and operational but not by much. It improved that by about 1 or 2 percent.

It didn't increase efficiency or productivity on our team because we had to spend more time trying to figure out how it worked rather than actually working with it.

It did help us to convert data into knowledge. It did provide a little bit of value.

What is most valuable?

In regards to removing null values and the like, it did do so but that's a pretty basic task. I don't need an entire tool to do something like that. I can do it very quickly in Python or R.

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

What needs improvement?

The solution's ability to capture complex relationships over time and the resulting accuracy of its predictions could be improved.

They could also improve customer relations with education on how to use the tool. It took us quite a while to figure out how things were put together so that we could get things to work and provide proper feedback to our leadership. 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. These things should be a priority for them, in my opinion. Knowing how to use the tool would have given us more time to play with it instead of just trying to figure it out. They should "game-ify" it more.

Darwin could do a few other things better such as automating determination of whether certain values need not be removed and that certain parameters should not be removed. And their UI could use some speeding up.

For how long have I used the solution?

I used it for three months to do a PoC.

What do I think about the stability of the solution?

Darwin is stable, but it just doesn't provide the functionality that an analyst would need.

How are customer service and technical support?

We asked questions and there were a couple of qualified people we spoke to at SparkCognition.

Which solution did I use previously and why did I switch?

We were previously using open-source software such as Python and R. We wanted something simpler and easier and faster to use. That's why we looked into SparkCognition.

How was the initial setup?

It took about two months to deploy it and we had one month for testing it out. Once we had direction, the setup was pretty straightforward. But before that, it was pretty complicated because we weren't certain about where to go or what to do.

Our strategy was to see how we could use the tool as a business analyst/low-level data-planning tool to and to see if we could improve processes by using it, even if we did have Python and R. But it was quicker to just Google around and find things online to fix stuff, rather than for a client of ours having to have to pay for this tool to make that happen.

What about the implementation team?

We did it all internally.

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 get people up and running on it quickly instead of spending weeks on end going back and forth to figure out logistics.

Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor. The reviewer's company has a business relationship with this vendor other than being a customer: Reseller.
TalhaKhwaja
Software Engineer (ML/CompVision) at a tech vendor with 51-200 employees
Real User
Top 5
It has good accuracy for solving complex problems

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 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.

How has it helped my organization?

We just load the data, then analyze the data. We just check if some of the data is missing or if there are some outliers in the data. We can analyze the data to see what are the relations between the columns of data.

We create the data and models with Darwin. We use those models and data in products. It's a great help.

It saves some time. Even people who are not fully technical can use it with a little guidance or something. It increases productivity.

Initially, I worked on creating a pipeline. I did this once. It has automatic AI building in its API pipeline.

What is most valuable?

I found Darwin a simple tool. It's a really helpful. It's easy to clean data forwards and backwards when we need to analyze it and to get a quick model.

The solution’s ability to capture complex relationships over time and the resulting accuracy of its predictions is good. You can solve complex problems with it. It's easy to use. It gets good accuracy.

I find it quite simple to use. Once you are trained on the model, you can use it anyway you want. 

It was quite comfortable to use.

What needs improvement?

The automatic generation of some models doesn't work. If it was automatic, this would accelerate the work that we do.

As a data scientist, I would find some other tools available for new methods which would be much more interesting because they would give me more control. However, for a normal person who is not yet a data scientist, Darwin would be more helpful for them.

The analyze function takes a lot of time.

For how long have I used the solution?

I have been working on Darwin for about two to three months.

What do I think about the stability of the solution?

Darwin is quite stable.

What do I think about the scalability of the solution?

if you give a lot of data to Darwin, sometimes it can hang. It can get stuck, e.g., if you were planning some big training and you want some big data. This is because the analyze function takes too much time. So, maybe scalability could be a problem.

We have about 10 users for Darwin, but not everyone is using it. Right now, two people are actively working on Darwin. The team using it is working on a small project.

How are customer service and technical support?

The technical support is quite responsive. It's good and fine.

Which solution did I use previously and why did I switch?

We were previously using open source tools, but nothing similar to Darwin.

How was the initial setup?

It was fairly straightforward.

The deployment took quite a while. It took maybe four and a half months.

We were not given all the data, and it was basically a prototype. It was given to the clients so they could see what the capabilities of the solution are. We had to generate some data and create frames, then we cleaned model. They developed the front-end and back-end, which they just deployed.

What's my experience with pricing, setup cost, and licensing?

As far as I understand, my company is not paying anything to use the product.

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.

Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.