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
Speakers
Jay Schuren
Chief Customer Officer
Ram Thilak
Group Head, Data Science & AI, Inchcape
Aditya Shankar
Regional Director, AI Success, APAC
DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards. We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence
DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.