DataRobot

We use automodelling (typically OTV binary regression), EDA tools and deployments.

We use DataRobot to generate Machine Learning models that inform the user experience on our website. The predictions are real-time. I have been most impressed with the simplicity in deploying models.

I used Data Robot to design a machine learning algorithm that profiled employee's work environment and absenteeism behaviour (as well as other market factors) to determine if they matched the profile of those employees who had left before us. We were able to use this information to understand the emerging turnover risk of our employees across our various facilities, managers, states and tenures.<br><br>The result allowed us to target our HR initiatives, provide additional training and support to staff and managers, and implement out of the box solutions to newly discovered issues resulting in turnover. It also allowed us to confirm and quantify the impact of different drivers on turnover, which in turn let us prioritise our responses. Finally, we were able to use the models to estimate the impact on turnover and costs a change initiative might cause by looking at the historical impact of initiatives run by our individual sites and/or how difference between a variable had impacted turnover previously.<br><br>Having access to data scientists and project management staff to help design, understand, train and utilize, identify use cases and design the change process was the highlight of their service.

As Australia's largest radiology business, we use it as part of our sales and outreach program to identify referring doctors who we need to prioritise contact with for a variety of goals including retention and growth. The models trained and deployed on DataRobot assist with this prioritisation process. The output of the model is effectively a risk or opportunity score on how likely a doctor will either increase or decrease in referrals to our clinics if we do not check in on them.

This is used for building data models for scorecard monitoring in our department, Risk Management.

Helping business partners optimize their marketing campaigns based on the strategic objectives of the specific campaigns.

The DataRobot has great cloud computing and multitasking power for feeding large quantity of data. There are time series, categorical, and numerical related target variable for analysis. The whole bucket preprocessing sometimes could help you to save a lot of times. However in the real scenario it would still be better to do most of the preprocessing manually to avoid confusion regarding data type. The post parameter tuning could be more intuitive and would be better to make the replication of model result easier.

We use DataRobot for a variety of use cases, such as automated quoting, forecasting/inventory management, and automation of machinery settings to improve manufacturing scrap rates.

DataRobot helps us make sense of a large amount of information. Trying to predict what's going to happen is always difficult, but with DataRobot we're able to use the power of robots to do the heavy lifting for us. Let's face it — even if we could do all the complex math on our own, the time it would take would be completely prohibitive. DataRobot takes all that on, letting us get to the decision making instead of setting up models.

We use the tool for student success. We make various predictive models for predicting the graduation and retention of our students. We use the predictions to identify at-risk students and refer them to advisors or success coaches for help. For example, if students have less than a 60% probability of graduating within four years based on the predictive model, they will get referred to an advisor for further help. During our use of the tool, we have almost doubled our 4-year graduation rate.

We build predictive models with the core supervised learning product. These include attribution models, churn/retention models, segmentation models, and others. Basically, anything that can be accomplished by taking a supervised, labeled set of training data and turning it into a predictive model, we use DataRobot. We have also dabbled with unsupervised learning and time series modeling but have not purchased those packages.

DataRobot is a very useful tool to quickly look for interactions and relations between different features. It serves as a comfortable way to engineer features and rapidly interpret behaviors in between specific attributes.

We use DataRobot as scoring to underwrite new policies. It is an additional help to know who will be a good insurer and who will not. You can use DataRobot to predict something that you need. Our business problem is that it is sometimes difficult to know who is going to have a claim and who doesn't.

Acting as a credit broker, I use DataRobot to allow me to rapidly build and deploy machine learning models to predict the likelihood that a loan application will be accepted by a lender; that the consumer will engage with the offer; that the consumer and lender will agree terms and a loan will be funded. Identifying populations with a low probability allows me to reduce costs to lenders in scoring loan applications; and improves the earnings per referral that our partners use to measure our performance.

DataRobot is used for training several models, evaluating the trained models, deploying them in production, and tracking the result on day to day basis. DataRobot has excellent MLOps support. We are utilizing DataRobot for demand prediction (time-aware models). In another project, we utilized the predictor and optimizer applications for demo purposes.

DataRobot is really useful for data scientists to start immediately and allows basic asks such as hyperparameter tuning, optimization of parameters, and choosing the right model easily. In the current organization, we use to production the solution and really helped reduce the execution timelines around the projects

We do not use DataRobot anymore. It was oversold by their sales guy. They have fantastic data scientists who solved the use cases we had, but from there we have not really used it. It turned out it was not the platform that solved the issues but the preprocessing step outside of DataRobot using a python package to do some specific calculations. The main drawback is the pricing structure. so that is what we are doing now. We have tried to do internal roadshows to see if other parts of the organization could use it, but no luck yet.

We use DataRobot to predict and classify tickets in our helpdesk system. This has helped to save considerable time in having to manually classify tickets. We have also used the DataRobot/Uipath connector to allocate the automatically classified tickets into the agents' queues without having them manually pick up the tickets for resolution.

An amazing data analytics platform. Cloud-based solution. User-friendly dashboard. A lot of customization can be done based on business requirements. Gives good output from the raw data. Lots of data fields are available. Nice chart functionality. Different roles are available. We can use it on Prem deployment also. Support multiple data formats. The use case was to increase the visibility of the customer current services which they are using.

DataRobot is super incredible with data engineering. It has a lovely UI. It's simple to deploy and use. Great with real-time reporting by intelligence.

We use DataRobot to address several uses cases across clinical, financial, and operational areas. In the clinical setting, we are looking to predict specific outcomes, like readmissions, in an effort to adjust reduce the readmission rate. The operations team uses outputs from DataRobot to forecast census levels that potentially impact our financials.

Datarobot is one of the best AutoML tools out there with many SOR models. It has helped our organization in realizing the potential of AI and AL. It is really easy to use and understand even for a budding ML engineer or data scientist. They also provide the best support and continuous improvement to the tool.

We have a multi-classification problem and a relevancy problem we are using DataRobot to provide us a model for.

I am one of several analysts at our organization who uses DataRobot for predictive modeling in an insurance context. Several of us are all working on models that are subsets of a large data set that we are hoping to use to help with future pricing efforts.

Used for research and prediction. Broad and across all aspects of our business. Helps us make use of historical datasets to better calibrate our models.