Why AI practitioners choose DataRobot
Collaborate in a centralized and interoperable workspace
Consolidate datasets, features, experiments, models, and deployments into a single experience. Eliminate roadblocks by connecting your data, infrastructure, tools, and business applications through pre-built integrations or our open API.
Ensure model quality from end-to-end
Maintain model integrity from experimentation to deployment. Customize preprocessing, feature engineering, model evaluation metrics, hyperparameter tuning, AI guardrails, and custom metrics at every stage of the process.
Explore at scale with speed
Evaluate and implement cutting-edge predictive models and LLMs. Rapidly experiment with hundreds of suggested approaches for data preprocessing, vector database chunking, multimodal feature engineering, and prompt engineering to maximize accuracy.
Streamlined processes and workflows
Shorten time to deployment by automating complex, repetitive, or manual steps, including data cleaning, compliance documentation, configuring AI pipelines, and managing deployment resources.
“We’ve automated the stuff that data scientists didn’t really like doing so they can focus on what really drives change. AI/ML has been critical in terms of the efficiency we’ve achieved by allowing us to scale massively.”
“Other AI products were trying to solve a specific problem. What I like about DataRobot is the ability to use it in any way you can think up, whether it’s a normal regression-type problem, or forecasting, or for many different use cases.”
“We chose DataRobot to be a part of our AI journey because IT. JUST. WORKS. The end-to-end capabilities, AI production, and the ability to own and control our AI IP have all been a game changer. Most importantly, DataRobot integrates easily within our AWS ecosystem.”
“No other AI solutions are as integrated, easy-to-use, standardized, and all-in-one as DataRobot AI 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.”