Many organizations struggle to manage and maintain their growing technology ecosystem while trying to generate more value from their AI initiatives. To systematically realize the value of AI at scale, there is a need for more workflow automation and integration of ML across business functions. Machine learning lifecycles need to be treated similarly to software development lifecycles, with continuous integration and continuous development.
This session will discuss:
- How to realize the value of AI and maintain that value over time
- Where to find value by embedding DevOps best practices in your ML lifecycle
- How Inchcape, a multinational automotive distribution leader, is generating value from AI at scale with DataRobot
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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