Junior Data Scientist at a tech services company with 51-200 employees
Real User
2019-12-09T10:59:00Z
Dec 9, 2019
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.
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.
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.
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.
Business Intelligence Director at a financial services firm with 51-200 employees
Real User
2019-12-05T06:53:00Z
Dec 5, 2019
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.
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.
Software Engineer (ML/CompVision) at a computer software company with 51-200 employees
Real User
2019-12-04T05:40:00Z
Dec 4, 2019
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.
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.
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.
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.
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.
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.
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.
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.
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.
The primary use case is to predict the default on payments by clients.