Darwin Reviews

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.

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.

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.

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.

I used it for three months to do a PoC.

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

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

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.

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.

We did it all internally.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

I have been using the solution for six months.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Darwin is quite stable.

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.

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

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

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.

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

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.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

We didn't test any other AI solutions.

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.

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

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.

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.

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.

We have been using Darwin for almost eight months.

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.

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.

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.

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.

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.

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.

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

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.