The Ever-growing Importance of MLOps: The Transformative Effect of DataRobot

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. This second part will dive deeper into DataRobot’s Machine Learning Operations capability, and its transformative effect on the machine learning lifecycle.  

DataRobot’s Robust ML Offering

DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents. These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. By leveraging continuous model competitions, DataRobot’s MLOps checks new models with its champion/challenger processes, along with production diagnostics designed to monitor service health over time. And with its “any model, anywhere” approach to AI deployment, teams can deploy any model to any production environment. With governed, secure, and compliant environments, data scientists have the time to focus on innovation, and IT teams can focus on compliance, risk, and production with live performance updates, streamed to a centralized machine learning operations system.

As organizations build out ML initiatives, model quantity in production grows as well as the task management of these models during their lifecycle. DataRobot MLOps counters potential delays with a management system that automates key processes. With secure workflows, hot-swap model approvals, and streamlined champion/challenger gating, DataRobot’s product ensures the efficient management of models’ lifecycle as efforts scale up.

Governance and Trust

All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent. The in-built, data quality assessments and visualization tools result in equitable, fair models that minimize the potential for harm, along with world-class data drift, service help, and accuracy tracking. 

In addition, DataRobot’s Bias and Fairness monitoring enables you to track when protected classes fall below predefined fairness thresholds and identify the cause. This capability is a vital addition to the AI and ML enterprise workflow. 

However, with these newfound benefits come challenges, with over 79% of organizations claiming to face governance, compliance, and audit challenges. 

DataRobot’s MLOps solution addresses these challenges with embedded governance tools. Model Approval Workflows allow teams to maintain thorough reviews of model updates with customizable and governed review cycles. Moreover, MLOps tracks and preserves all prediction activity and model updates. And to keep the user experience consistent and streamlined, MLOps provides a single place to register all models regardless of their origin, allowing you to deploy, replace, and manage models from one central location.

A Look to the Future

MLOps allows organizations to stand out in their AI implementation. And DataRobot’s MLOps accelerates getting Machine Learning into production, thereby reducing spending on infrastructure and sparing data science talent from mundane tasks. Models are deployed faster to optimize outcomes, reduce costs, and maximize ROI, all with full governance and trust.