DataRobot

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.

I am using datarobot to develop Application and Behavioural Credit Scorecards for the Bank. Develop credit risk models to be used for various business operations (i.e., Products, Credit, and Collections), such as cross-selling, credit limit selling, and collection strategy formulation. Develop credit risk models to elevate lending decision-making and enhance risk management at CIMB PH.

We use it to accelerate the building and deployment of our ML platform in our context to solve problems for financial applications.

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 builds machine-learning models to predict creditworthiness of bond issuers.

DataRobot is part of our DataScience LifeCycle supporting our team in every stage of the AML process. For each business area in my company, data robot contribute reducing the time for development and also reducing the learning curve when we hire a new member in our team. In terms of scope, i have used DataRobot only for batch predictions and it works very well.

I use DataRobot to forecast our sales each month. DataRobot builds models from the data we give it and then gives us a prediction on what sales are going to be like for the next 90 days. I then use that data to determine how many products we need to purchase for a particular item (in our case, medical apparel). The balance we are trying to find is having enough inventory to sell so we are not out of stock on a particular item while not having too much of our capital tied up into inventory that is not selling. One problem with selling medical apparel is sorting thru all the data and figuring out what data is consequential or what is not (for example, the size of a piece of clothing is consequential, like Large will sell more than X-Small, but fabric type may be less consequential). DataRobot allows me to use machine learning technology to go thru many different data points and to see what is consequential and what is not.

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.

Exploratory data analysis including time series as well as forecasting and building prediction models.

We use DataRobot to build data products such as predictive models that are helping the business to move the needle. We use them mainly to adapt consumer experience now that PMI has become a consumer-facing company (it was not the case before IQOS) This year we are starting a new way of engaging with the business to make them part of the full process.

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 supports prioritized AI/ML use cases, which range from predictive models around financial performance to employee-focused use cases.

We use DataRobot for predictive analytics. We predict student outcomes (to maximize), student exits (to minimize), and such key business priorities based on their previous performance, socio-economic attributes, behavior, wellbeing, school attributes, community attributes, etc. Over 50 input variables in one regression model. Similarly, over 20 variables in another logistic regression model. Use cases are emerging in technology and facilities areas too.

DataRobot in my organization is used to automatically hibernate alerts to reduce the investigation load for anti-money laundering activities. First, the DataRobot was fed with training data and once the model was trained, we feed each month's data into DataRobot and based on the model it is able to hibernate up to 25 percent of the alerts resulting in less caseload on the investigators. DataRobot is very convenient to use and even if someone doesn't have any background knowledge about machine learning and zero programming experience can still use and deploy machine learning models with ease.

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.

At MCA, we are continuing to find new and unique ways to integrate modeling into our work effort and client interactions. We are still early in our thinking and maturity in the modeling space, the potential is high and we appreciate the partnership and service we get from the DR team.

Accelerating predictive model development through automated machine learning and providing a seamless and very user-friendly ability to deploy models instantaneously are the most important drivers of success we have gained from [the] use of DataRobot. We have a live prediction model that handles hundreds of predictions a day, with turnaround times of

DataRobots helps us to understand and use models in reality. Efficient in model configuration and selection. Great in documenting business models. We use it to clean large data and create data flow and automation. Prediction on continuous and categorical data. Connecting to data sources. Efficient in analyzing data and identifying indicators.

DataRobot has been an absolutely game-changing platform. Using DataRobot, we've been able to introduce several different machine learning models incorporating mixed data types into robust tools to assist our university community in decision making, forecasting of different KPI, such as retention and graduation rates, and even in tracking and monitoring students that are at risk of stopping out.

The insights from the platform are unparalleled, allowing you to quickly see what factors are influential in any predictions being made, as well as giving the user the ability to pose what-if types of analyses. These tools have allowed our small data science team to accomplish absolute wonders and in record timing!