DataRobot Solutions for the Public Sector

Unlike other tools that provide limited automation for select portions of the data science workflow, our Automated Machine Learning product automates all of the steps needed to build, deploy, and maintain powerful AI applications at scale. This includes enabling both novice and expert users to quickly and interactively explore, profile, clean, enrich and shape diverse data into AI assets ready for machine learning model development and deployment.

Our experienced data science teams are constantly adding the latest open source machine learning algorithms to the platform, with unique blueprints that automatically optimize data preprocessing, feature engineering, and tuning parameters for each algorithm. DataRobot builds and ranks dozens of models for each AI use case and recommends the best model to deploy. Then, monitors all models in production so you always have the best AI possible.

Data scientists gain access to the largest library of powerful open-source algorithms, and can automate repetitive and time-consuming tasks so they can focus on AI value creation.And, empowered and enabled citizen data scientists can use their unique domain knowledge to build machine learning models and increase your overall capacity for AI.

The goal of time series modeling is to predict future performance from past behavior – such as forecasting sales over a holiday season, predicting how much staff you need for the upcoming week, or ensuring inventory meets manufacturing demands without overstocking.

Unfortunately, time series modeling can be a complex and laborious process because many historical events can impact the current predictions, and finding the most influential signals is difficult. As the environment changes, such as after introducing a new product or a competitor opening a new store, these models need to be manually re-built. Until now!

Beyond essential and proven times series methods like ARIMA and Facebook Prophet, DataRobot includes advanced time series models that help you achieve even higher forecasting accuracy.

Since the goal of a time series model is to both extract understanding and predict future outcomes, DataRobot offers many ways to visualize insights over time and to deploy models to production - including full API support to integrate modeling into business processes and applications.

MLOps delivers the capabilities that Data Science and IT Ops teams need to work together to deploy, monitor, and manage machine learning models in production and to govern their use in production environments. With DataRobot MLOps and Governance, organizations can:

• Easily deploy machine learning projects from any ML platform on modern production infrastructures such as Kubernetes and Spark on any cloud or on-premise.
• Monitor ML-based applications for performance issues with ML-centric capabilities like data drift analysis, model-specific metrics and infrastructure monitoring and alerts.
• Manage the dynamic nature of machine learning applications with the ability to frequently update models, test new, competitive models, and change applications on-the-fly while continuing to serve business applications.
• Enforce governance policies related to ML deployment and capture the data that is required for strong governance practices, including who is publishing models, why changes are being made, and what models were in place over time.