Trust is an essential part of doing business. Whether it is the reliability of the supply chain, the accuracy of financial predictions, or the assurance of product availability, trust from customers, vendors, and suppliers is non-negotiable. For businesses that are AI-driven, this trust hinges on the confidence that their AI solution can help them make their most critical decisions.
In our ebook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. Our ebook covers the importance of secure MLOps in the four critical areas of model deployment, monitoring, lifecycle management, and governance.
We also look closely at other areas related to trust, including:
- AI performance, including accuracy, speed, and stability
- AI operations, including compliance, security, and governance
- AI ethics, including privacy, bias and fairness, and explainability
- How MLOps helps bridge the production gap between systems and teams
DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
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