Operationalizing machine learning (sponsored by IBM)
Machine learning research and incubation projects are everywhere, but less common, and far more valuable, is the innovation unlocked once you bring machine learning out of research and into production. Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes and in the models themselves that cause expensive and time-consuming misunderstandings.
| Talk Title | Operationalizing machine learning (sponsored by IBM) |
| Speakers | Dinesh Nirmal (IBM) |
| Conference | Strata Data Conference |
| Conf Tag | Big Data Expo |
| Location | San Jose, California |
| Date | March 6-8, 2018 |
| URL | Talk Page |
| Slides | |
| Video | Talk Video |
The rules of business are being rewritten because of abundant data and compute power, and machine learning research and incubation projects are everywhere. Less common, and far more valuable, is the innovation unlocked once you bring machine learning out of research and into production. But how do you easily build and operationalize machine learning systems at scale? Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes and in the models themselves that cause expensive and time-consuming misunderstandings. This keynote is sponsored by IBM.
