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.
Dinesh Nirmal is vice president of development for data and AI at IBM. His mission is to empower every organization to transform their industry—whether it’s aerospace, finance, or healthcare—by unlocking the power of their data. Dinesh speaks and writes internationally on operationalizing machine learning and advises business leaders on strategies to ready their enterprises for new technologies. He leads more than a dozen IBM Development Labs globally; recognizing a market need for data science mastery, he launched six machine learning hubs to work face-to-face with clients. Products in his portfolio regularly win major design awards, including two Red Dot Awards and the iF Design Award. Dinesh is a member of the board of the R Consortium and an advisor to Accel.AI. He lives in San Jose with his wife Catherine Plaia, formerly an engineer at Apple, and their two young sons.
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