My main use case for DataRobot is that it is a platform at an enterprise AI level that every organization uses to build, deploy, and govern each machine learning model at scale. It is basically an experiment to build, monitor, and govern AI models and also recognize some leadership in AI governance and ML operations, with nearly half of the Fortune 500 companies using it. I generally use DataRobot in healthcare projects. We integrate DataRobot into our AWS ecosystem, and it improves our patient healthcare through predictive analytics, resulting in fast diagnostics and better resource allocation. Efficiency in production and predictive maintenance are key aspects of my main use case with DataRobot.
My main use case for DataRobot is to give an agentic AI flavor to my different customers because many of my customers are looking for a consumption tool when they are looking to implement GenAI in their premises. DataRobot actually helps to create agents directly, both on-premises as well as on a cloud. We are an on-premises company, so I propose DataRobot solutions when customers are looking to actually integrate AI agents with their infrastructure which they have recently procured from Dell. We were working with a very large bank and they wanted to have an AI consumption tool where they can build AI/ML pipelines and they needed to have a graphical user interface where they can actually chat with the models which they have imported directly from outside as well as create agents which they can interface with their models as well as their commands. Based on their requirement, we zeroed in on a DataRobot solution because that actually helped them achieve all of their outcomes. We understood the use case, what the customer is looking to implement and we got up with the DataRobot team. We understood that they could actually cater to all of the requirements of the customer, then we went ahead with the deployment of DataRobot. DataRobot actually helped set up a multi-agent scenario for the customer and one agent talking to the other agent has automated the complete sequence of events of fraud monitoring where if one particular fraud is reported, the second agent can actually log it into the ledger books and that can be reported into the chief manager who can actually take it up where the exact issue is happening. The whole process gets automated. Previously my customer used to do everything manually, but now they are using agents to actually talk to their models as well as to their financial repository of information which they have brought into the vector database which comes along with DataRobot. They have actually automated several procedures such as updating the ledgers, updating the bank account information, generating feedback about their customer service. Everything is being automated. DataRobot is one of the major platforms being used, which actually interfaces with the primary bank application which they have in the particular bank. Model benchmarking actually helps to make sure that the results which are being provided by the model are correct. They can continuously review whether it has the right results which are being shown to the bank application and that will help them automate all of their remaining use cases which they are currently looking to deploy. Previously we had five or six processes which used to be done manually by different people and that has been transformed using DataRobot because agents now are doing the same thing. There is a lot of money saved. The manager mentioned that they have redirected the employee base to other tasks and they are incurring a cost savings of around $1,000 per employee and that has actually boosted the share of the company by a lot. Since it is a government PSU bank, we cannot share the financials, but they have actually achieved a lot of cost savings, around $2 million they have saved by implementing DataRobot.
DataRobot serves as our data science platform for building machine learning models and the development environment for running models. We also use the best practice processes and governance that DataRobot provides, and we are interested in leveraging the commercial value that DataRobot enables. A specific example of how I use DataRobot in my work is the speed at which you can get a model to market and the ability to manage models in production, which is excellent. We also use DataRobot to deploy models and deploy AI solutions that are straightforward and do not require heavy processing. We have integrated this software into the toolbox of our data scientists so that they are able to produce production-grade models in a very short time.
My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of the examples is I use DataRobot to predict which students are likely to accept the university offer. It basically helps us and the admission team to focus their efforts more efficiently. It also helps us with data matching and cleaning in large data sets, which reduces manual work. The prediction helps our team and the admission team to prioritize outreach to the students who are most likely to accept the offer. They inform marketing and follow-up strategies as well, making efforts more efficient and quicker. One example is if DataRobot predicts a student has a high likelihood of accepting, the team can send personalized emails or call them to provide guidance and support directly to these students. It basically focuses on these specific students which have been just highlighted by DataRobot. It also reduces time spent on students who are unlikely to enroll, allowing us to use our resources more efficiently only on the people who we think are actually going to come back and enroll with us. I do use DataRobot for many other things as well. For example, other than the target of student enrollment, I use DataRobot for data cleaning. I do the cleaning of deduplication as well. I also use this to detect any anomalies. It basically helps me to automate all the repetitive tasks and saves me some time. One example I can share is I use it to flag duplicate student records across multiple systems, which used to take us hours to do before, and now it's done a lot more quickly by using DataRobot.
Staff Specialist Data Scientist at a tech vendor with 5,001-10,000 employees
MSP
Top 10
Feb 12, 2025
In our day-to-day use, I utilize DataRobot to speed up our development process through its GUI capability. Once I set up our connection with a back-end data set, whatever the project I work on next, it automatically integrates the data and catalogs. I can continue with feature engineering, prediction modeling, and deployment all in one place. I bring in data and train models using GUI and API methods.
Head of Data and Analytics at a manufacturing company with 501-1,000 employees
Reseller
Apr 4, 2022
DataRobot is used to create models and monitor them as an MLOps. Additionally, to work as pre-processing, to create more features based on their data. It's helping build models much better. By using DataRobot you are using data science capabilities. The type of deployment model depends on the company, but a lot prefer the cloud rather than on-premise. However, some specific data needs to be not on-premise. Most companies want to be in the cloud, but not all the data is relevant there.
Data Scientist at a tech services company with 11-50 employees
Real User
Dec 11, 2019
We are a healthcare startup, so we work with both the healthcare insurance providers as well as the hospitals. We are developing a robotic process automation system for both the insurance part and the hospital side of the healthcare industry. In that operating process automation, we plan to imbibe intelligence, with the help of machine learning.
DataRobot automates model building and deployment, simplifying MLOps with user-friendly interfaces. Its AutoML and feature engineering streamline model comparison, selection, and testing, enhancing efficiency and scalability.DataRobot facilitates efficient integration with cloud systems and data sources, reducing manual workload, enhancing productivity, and empowering data-driven decision-making. Its strengths lie in automating complex modeling tasks and supporting multiple predictive models...
My main use case for DataRobot is that it is a platform at an enterprise AI level that every organization uses to build, deploy, and govern each machine learning model at scale. It is basically an experiment to build, monitor, and govern AI models and also recognize some leadership in AI governance and ML operations, with nearly half of the Fortune 500 companies using it. I generally use DataRobot in healthcare projects. We integrate DataRobot into our AWS ecosystem, and it improves our patient healthcare through predictive analytics, resulting in fast diagnostics and better resource allocation. Efficiency in production and predictive maintenance are key aspects of my main use case with DataRobot.
My main use case for DataRobot is to give an agentic AI flavor to my different customers because many of my customers are looking for a consumption tool when they are looking to implement GenAI in their premises. DataRobot actually helps to create agents directly, both on-premises as well as on a cloud. We are an on-premises company, so I propose DataRobot solutions when customers are looking to actually integrate AI agents with their infrastructure which they have recently procured from Dell. We were working with a very large bank and they wanted to have an AI consumption tool where they can build AI/ML pipelines and they needed to have a graphical user interface where they can actually chat with the models which they have imported directly from outside as well as create agents which they can interface with their models as well as their commands. Based on their requirement, we zeroed in on a DataRobot solution because that actually helped them achieve all of their outcomes. We understood the use case, what the customer is looking to implement and we got up with the DataRobot team. We understood that they could actually cater to all of the requirements of the customer, then we went ahead with the deployment of DataRobot. DataRobot actually helped set up a multi-agent scenario for the customer and one agent talking to the other agent has automated the complete sequence of events of fraud monitoring where if one particular fraud is reported, the second agent can actually log it into the ledger books and that can be reported into the chief manager who can actually take it up where the exact issue is happening. The whole process gets automated. Previously my customer used to do everything manually, but now they are using agents to actually talk to their models as well as to their financial repository of information which they have brought into the vector database which comes along with DataRobot. They have actually automated several procedures such as updating the ledgers, updating the bank account information, generating feedback about their customer service. Everything is being automated. DataRobot is one of the major platforms being used, which actually interfaces with the primary bank application which they have in the particular bank. Model benchmarking actually helps to make sure that the results which are being provided by the model are correct. They can continuously review whether it has the right results which are being shown to the bank application and that will help them automate all of their remaining use cases which they are currently looking to deploy. Previously we had five or six processes which used to be done manually by different people and that has been transformed using DataRobot because agents now are doing the same thing. There is a lot of money saved. The manager mentioned that they have redirected the employee base to other tasks and they are incurring a cost savings of around $1,000 per employee and that has actually boosted the share of the company by a lot. Since it is a government PSU bank, we cannot share the financials, but they have actually achieved a lot of cost savings, around $2 million they have saved by implementing DataRobot.
DataRobot serves as our data science platform for building machine learning models and the development environment for running models. We also use the best practice processes and governance that DataRobot provides, and we are interested in leveraging the commercial value that DataRobot enables. A specific example of how I use DataRobot in my work is the speed at which you can get a model to market and the ability to manage models in production, which is excellent. We also use DataRobot to deploy models and deploy AI solutions that are straightforward and do not require heavy processing. We have integrated this software into the toolbox of our data scientists so that they are able to produce production-grade models in a very short time.
My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of the examples is I use DataRobot to predict which students are likely to accept the university offer. It basically helps us and the admission team to focus their efforts more efficiently. It also helps us with data matching and cleaning in large data sets, which reduces manual work. The prediction helps our team and the admission team to prioritize outreach to the students who are most likely to accept the offer. They inform marketing and follow-up strategies as well, making efforts more efficient and quicker. One example is if DataRobot predicts a student has a high likelihood of accepting, the team can send personalized emails or call them to provide guidance and support directly to these students. It basically focuses on these specific students which have been just highlighted by DataRobot. It also reduces time spent on students who are unlikely to enroll, allowing us to use our resources more efficiently only on the people who we think are actually going to come back and enroll with us. I do use DataRobot for many other things as well. For example, other than the target of student enrollment, I use DataRobot for data cleaning. I do the cleaning of deduplication as well. I also use this to detect any anomalies. It basically helps me to automate all the repetitive tasks and saves me some time. One example I can share is I use it to flag duplicate student records across multiple systems, which used to take us hours to do before, and now it's done a lot more quickly by using DataRobot.
In our day-to-day use, I utilize DataRobot to speed up our development process through its GUI capability. Once I set up our connection with a back-end data set, whatever the project I work on next, it automatically integrates the data and catalogs. I can continue with feature engineering, prediction modeling, and deployment all in one place. I bring in data and train models using GUI and API methods.
We work on AI and ML use cases related to technology and IT.
I did a proof of concept (POC) at DISH Wireless (company name) before they were about to sign a contract. Currently, I'm working on another POC.
DataRobot is used to create models and monitor them as an MLOps. Additionally, to work as pre-processing, to create more features based on their data. It's helping build models much better. By using DataRobot you are using data science capabilities. The type of deployment model depends on the company, but a lot prefer the cloud rather than on-premise. However, some specific data needs to be not on-premise. Most companies want to be in the cloud, but not all the data is relevant there.
We are a healthcare startup, so we work with both the healthcare insurance providers as well as the hospitals. We are developing a robotic process automation system for both the insurance part and the hospital side of the healthcare industry. In that operating process automation, we plan to imbibe intelligence, with the help of machine learning.