Ever Growing Importance of MLOps Tranformative Effect of DataRobot BKG

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

February 11, 2022
· 2 min read

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. 

MLOps for IT Teams: How to Transform the Machine Learning Lifecycle
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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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