IT Operations

Put machine learning models into production with just a few lines of code, regardless of whether you need real-time predictions, batch deployments, or scoring on Hadoop.

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Benefits to IT Operations

Machine learning for today's enterprise

Machine learning and AI projects that never reach production can have a significant impact on the bottom line, and raise doubts about the ROI on data lakes and other big data investments. DataRobot automates the best practices of the world's top data scientists in software – making it easy for business analysts, data scientists, software developers, and other data professionals to rapidly build and deploy highly accurate machine learning models.

Deploy with one line of code

Any model built in DataRobot is production ready. There is no need to write custom prediction code or manage any infrastructure. Use DataRobot's dedicated API to deploy models to servers behind a load balancer, easily scaling to meet your needs. Need to update your model every day? Automate the process with DataRobot's APIs and SDK.

Scale with Hadoop

DataRobot can be run on your Hadoop cluster to bring fully-integrated automated machine learning to the Hadoop stack. DataRobot utilizes Spark, and is certified compliant for data security and management so machine learning data objects and operations are visible to, and under the control of, Hadoop management processes and policies.

Create great models with full transparency

With DataRobot you can build and test hundreds of solutions that combine expert-level data preparation with machine learning algorithms from R, Python, Spark, H20, TensorFlow, and more. Use our industry-leading GUI or powerful Python and R APIs to compare solutions for model accuracy, prediction speed, and simplicity to find the best fit for your use case.

Empower your organization

DataRobot gives the power of automated machine learning to users of all skill levels. It provides access to a vast array of the latest and most powerful machine learning algorithms, yet puts appropriate guardrails in place so that even inexperienced users can select the best algorithms and pre-processing steps for the business problems they are trying to solve.

Deploy with one line of code

Any model built in DataRobot is production ready. There is no need to write custom prediction code or manage any infrastructure. Use DataRobot's dedicated API to deploy models to servers behind a load balancer, easily scaling to meet your needs. Need to update your model every day? Automate the process with DataRobot's APIs and SDK.
Scale with Hadoop

DataRobot can be run on your Hadoop cluster to bring fully-integrated automated machine learning to the Hadoop stack. DataRobot utilizes Spark, and is certified compliant for data security and management so machine learning data objects and operations are visible to, and under the control of, Hadoop management processes and policies.
Create great models with full transparency

With DataRobot you can build and test hundreds of solutions that combine expert-level data preparation with machine learning algorithms from R, Python, Spark, H20, TensorFlow, and more. Use our industry-leading GUI or powerful Python and R APIs to compare solutions for model accuracy, prediction speed, and simplicity to find the best fit for your use case.
Empower your organization

DataRobot gives the power of automated machine learning to users of all skill levels. It provides access to a vast array of the latest and most powerful machine learning algorithms, yet puts appropriate guardrails in place so that even inexperienced users can select the best algorithms and pre-processing steps for the business problems they are trying to solve.

How Can You Scale AI without Losing Control Over Your Models?

Achieving success with AI can be hard. But it gets even more complicated once you start scaling. How can you ensure that IT teams have the necessary model monitoring and governance infrastructure to maintain your machine learning models? Learn how Machine Learning Operations (MLOps) allows organizations to mitigate many of the related issues by providing support for managing the machine learning lifecycle through automation and scalability.

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