DataRobot is a machine learning AI cloud platform that enables data scientists and enterprises to automate, guarantee and accelerate predictive analysis.

The platform is a credible alternative to Google's Cloud AutoML, which has historically been considered the gold standard in the field.

Improving on Google's model, the company has expanded to cover the entire machine learning lifecycle, from preparing training data sets to deploying and training models.

DataRobot makes it easy to use and optimize the most valuable open-source modeling techniques from R, Python, Spark, H2O, VW, XGBoost, and many others.

It helps data scientists and analysts build and deploy accurate predictive models in a fraction of the time required by other solutions. This allows teams of experienced or new data scientists to simplify and accelerate machine learning and optimize scarce and expensive data science skills.

Given a problem to solve (a financial prediction, preventive maintenance, image recognition, etc.), DataRobot's Auto Machine Learning brick selects and trains several possible algorithms.

Then, it scores them by mixing several combinations of various relevant hyperparameters to retain only the best-performing model. As a data scientist, this allows you to save time in your analyses and ensure the quality of the results you get.

It helps companies quickly build and deploy hundreds of high-quality models—without risk or time loss—thus multiplying your productivity and the quality of your models.

The strength of the DataRobot platform lies in its ease of use, even for data analysts or business experts. And this is for creating as well as putting models into production.

Another strong point claimed by DataRobot is the governance workflow orchestration from design to implementation of models. DataRobot has also beefed up this MLOps solution to support models developed by hand in Python or R.

Key features

Self-service data preparation
Automated machine learning (auto ML)
Centralized notebook management
No code development of AI applications
MLOps to quickly deploy models from a variety of languages and frameworks
Seamless collaboration with fellow data scientists on a single platform

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