DataRobot Frequently Asked Questions
Find answers to our most commonly asked questions about DataRobot AI Cloud
Organizations rely on DataRobot for end-to-end AI and machine learning lifecycle management — from data preparation to model development with AutoML to model deployment and monitoring with MLOps. Simplify collaboration across analysts, business stakeholders, data engineers, data scientists, machine learning engineers, and IT teams.
Trusted AI and Explainable AI simplify compliance for highly regulated industries including banking, financial services, and healthcare through guardrails and automatically generated compliance documentation.
How do organizations use DataRobot?
DataRobot customers use DataRobot AI Cloud to increase their productivity and efficiency related to machine learning projects and AI applications. Leading companies around the world, including a third of the Fortune 50, trust DataRobot to accelerate the delivery of AI into production.
DataRobot allows us to try dozens of machine learning techniques in a short period of time. Then, we just need to pick the best-performing model, which is ready to be deployed and consumed by our decision engine to make more informed lending decisions.Cecilia Lopez
Head of Data Science, Carbon Digital Bank
What does DataRobot do for organizations that want to accelerate the delivery of AI?
DataRobot provides a unified environment built for continuous optimization across the entire AI lifecycle. DataRobot AI Cloud platform simplifies data preparation, automates machine learning, and provides a center of excellence for your production AI.
From ingesting the data to performing data quality to developing and testing models to deploying them… the platform does everything for us with minimal manual intervention. I haven’t found another tool that does that.Chris Mendoza
Director of Financial Crimes Technology, Valley National Bank
What is DataRobot AI Cloud?
DataRobot AI Cloud is the next generation of AI. DataRobot AI Cloud allows organizations to scale their data science infrastructure securely and cost-effectively, without having to commit to one cloud vendor for storage, compute, and machine learning.
No other AI solutions are as integrated, easy to use, standardized, and all in one as DataRobot AI Cloud Platform. DataRobot provided us with a structured framework to ensure everybody has the same standard when it comes to the more tedious and repetitive parts of machine learning. It really allowed us to spend our time more efficiently as a team.Thibaut Joncquez
Director of Data Science, Turo
What is DataRobot AutoML?
Short for “automated machine learning”, AutoML automatically builds machine learning models from raw data. Business professionals and data scientists can develop dozens of models, find insights and predictions, and solve business problems in days rather than the traditional weeks or months.
DataRobot AutoML automates many of the tasks needed to develop AI and machine learning applications. Organizations save time and money by taking advantage of:
- Automated feature engineering, feature discovery, and feature reduction
- Automated model development that trains tens to hundreds of models in parallel
- Automated data quality and target leakage checks
DataRobot’s gotten pushed into the hands of more data scientists that wouldn’t be doing machine learning before, but now they can actually deploy models, and deploy it smartly. It’s been a game-changer for us.Brad Boehmke
Director of Data Science, 84.51° (Kroger)
What is DataRobot MLOps?
Short for “machine learning operations”, MLOps provides the technology and practices to deploy, monitor, manage, and govern machine learning in production. MLOps is required to scale the number of machine learning-driven applications in an organization. It frees up data scientists to do their own work by empowering the MLOps engineers to take ownership of and responsibility for managing machine learning in production.
DataRobot MLOps provides a center of excellence for your production AI. This gives you a single place to deploy, monitor, manage, and govern all your models in production, regardless of how they were created or when and where they were deployed.
Model deployment in our legacy environment was complicated and it could take us three to four months to complete the transition. With DataRobot, model deployment is trivial and can be achieved in a few mouse clicks. This is a huge enabler for our business. Working at a higher cadence at the interface between data science and IT engineering creates faster returns on our investment in machine learning.Andrew Cathie
Chief Data Scientist, Harmoney
Is DataRobot cloud based?
Owning your AI means giving you the choice to run your AI wherever you want. DataRobot is a highly portable system that can be based in:
- All major cloud platforms including Amazon AWS, Microsoft Azure, and Google Cloud Platform
- On-premises data centers
DataRobot also embraces multi-cloud, giving you the freedom to deploy models into multiple environments at once. This level of flexibility avoids the need to lock into any single technical infrastructure or cloud platform. Of course, if you want to remove the headache of managing your AI system entirely, you can leverage our fully managed AI cloud service.
Who uses DataRobot?
Executives and analytics leaders find opportunities to apply AI through DataRobot, creating a culture that embraces data-driven decision making. Empower your team with the strength of data science to drive ROI faster.
Business analysts automate many of the routine tasks performed by data scientists using DataRobot, providing a leg up for incorporating machine learning into existing workflows. Drive high value for the business by supercharging your insights.
Data scientists shorten AI/ML projects that typically take weeks and months to hours and days by eliminating the distractions and time commitments of low-level details. Quickly share and hand off projects between teams and easily access capabilities for centralized management and monitoring, compliance, and continuous optimization.
Data engineers, DevOps, and IT teams deploy, monitor, manage, and govern all production models in a single place, regardless of how the models were created or when and where they were deployed. Make it easy for teams to rapidly build and deploy highly accurate machine learning models with scale.
What DataRobot really did was open up data science to everybody at Demyst. We now have salespeople competing in Kaggle competitions to get a better feel for the software. They were able to speak the language and upload the dataset, press one button, and get a result. That really got them excited about data science.Jason Mintz
VP of Product, DemystData
How does DataRobot simplify work for data scientists?
Organizations democratize machine learning and AI by using DataRobot to empower data analysts and citizen data scientists with machine learning capabilities. Democratizing and automating tedious, redundant machine learning tasks allow data scientists to focus on more strategic initiatives that require their expertise, saving time and resources.
Many data scientists also use AutoML to automatically sift through countless modeling possibilities, pinpointing the best models for their needs. Code-first data scientists can customize these models to best solve the business problem. Combining code with automated processes minimize the weeks and months it takes to build and train models to hours and days.
DataRobot AI Cloud enables us to scale with the limited resources we have so we can deploy multiple projects in parallel. It simplifies my job to deliver better solutions internally and allows us to perform more projects with the same head count. We now get models into production in seconds so we can spend more of our time on understanding the problem and the data.Nacho Vilaplana
Lead Data Scientist, Euskaltel
How are black box models handled?
DataRobot Explainable AI helps you understand the behavior of models and inspire confidence in their results. When AI is not transparent, it can be difficult to trust the system and translate it into business outcomes. With Explainable AI, you can easily understand the decision-making process of models and bridge the gap between development and actionable results.
DataRobot Explainable AI delivers explainability at all stages of the AI lifecycle.
Everyone is strapped for resources and stretched for time. AI and machine learning allow us to do more with fewer resources, faster. With DataRobot as our co-pilot navigating the journey, we give our team more hands, more eyes, more visibility into the data.Shadi Khatib
Chief Information Officer, Flexiti
How does DataRobot handle model bias and ethics?
DataRobot offers Bias and Fairness tools to test your models for bias and help you perform root cause analysis. Users can also mitigate bias with our no-code, out-of-the-box solution. Fix issues before they materialize and make appropriate trade-offs between model bias and accuracy.
If there is bias, we can mitigate it.Rachik Laouar
Head of Data Science, The Adecco Group, UK & Ireland
How does DataRobot satisfy model compliance for highly regulated industries such as financial services, healthcare, and government?
Using DataRobot automated and continuous machine learning platform, modelers can build cutting edge machine learning models for their business applications and have access to tools for automating many of the steps as mandated by their model risk management (MRM) framework. These include:
- A framework to build technically sound models that are easy to interpret
- A means to produce automated model documentation, for both models built in the platform, as well as custom models developed by your data scientists
- A means of monitoring model health and performance in production with DataRobot MLOps, ensuring the model is working for its intended business purpose
By streamlining the steps required in a modern MRM framework, data scientists are able to focus on the business impact of the models they produce, all while being compliant to the relevant regulations.
We use DataRobot to prototype new models that might solve business problems with the data that we have in our data warehouse. The models that have a positive return and fit in our business process are then evaluated and documented, and presented to a commission that approves them. Once they are approved, we also use DataRobot to deploy those models in production and monitor and govern the model in production, measuring the value that it is generating through time.Ignacio Vilaplana
Lead Data Scientist, Euskaltel