데이터 사이언티스트
Automating data quality, feature engineering, model building, and deployment process to enhance data scientists.
Providing Business Value
DataRobot has been designed to help data scientists wring value from every project. With an emphasis on supporting techniques like supervised machine learning and transfer learning, the platform also includes features that ensure business value like profit curves, prediction explanations, and one-click deployment with governance. All of this is integrated with automation to ensure your time is spent providing business value.
Designed for Enterprise Data Scientists
DataRobot carefully curates and validates algorithms from R, Python, Spark, TensorFlow, and other sources. We spend thousands of hours analyzing what Kaggle techniques hold up. Then, our machine learning engineers incorporate these techniques, while ensuring they run quickly, scale to large datasets and catch edge cases like missing values and new levels. Finally, we continually run thousands of automated tests to ensure our models are reliable.
Simplify Model Deployment
DataRobot starts by letting you select which model you want to deploy from hundreds of possibilities. With the powerful DataRobot API, you can immediately put any model into production with just a few lines of code, regardless of whether you need real-time predictions, batch deployments, or scoring on Hadoop. Update your models with no downtime and easily build continual learning pipelines.
Solving the Hardest Data Science Problems
Whether you are trying to forecast sales for a million products or working with extremely wide genomic data, DataRobot is capable of solving the hardest data science problems. Using our API, data scientists will often build hundreds or thousands of models to solve problems, all of which is easily done with our R and Python clients designed for data scientists.
-
Designed for Enterprise Data Scientists
![data-scientist-programmer-nerd-user-female]()
DataRobot carefully curates and validates algorithms from R, Python, Spark, TensorFlow, and other sources. We spend thousands of hours analyzing what Kaggle techniques hold up. Then, our machine learning engineers incorporate these techniques, while ensuring they run quickly, scale to large datasets and catch edge cases like missing values and new levels. Finally, we continually run thousands of automated tests to ensure our models are reliable. -
Simplify Model Deployment
![product-service-box]()
DataRobot starts by letting you select which model you want to deploy from hundreds of possibilities. With the powerful DataRobot API, you can immediately put any model into production with just a few lines of code, regardless of whether you need real-time predictions, batch deployments, or scoring on Hadoop. Update your models with no downtime and easily build continual learning pipelines.
