The Rise of Enterprise AI Platforms

With more AI use cases across industries than can be counted, it is clear that AI models have and will continue to cause lasting change across organizations. However, the successful productionization of AI models remains a challenging process. This panel features technical leaders within the data science groups at enterprise companies who discuss best practices for the operations surrounding AI production.

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(NASDAQ: INOD) Innodata is a global data engineering company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers.

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Your company’s Artificial Intelligence project will fail if you don’t do this

An interview with Rahul Singhal, the Chief Product and Marketing Leader for Innodata at the Open Data Science Conference. C-suite leaders are becoming increasingly frustrated that many of their Artificial Intelligence projects are failing. There is strong evidence that one of the core factors contributing to this high failure rate is poor data annotation. Rahul explains the issue.

Innodata's Chief Product Officer, Rahul Singhal

Rahul Singhal- Chief Product & Marketing Officer

As Chief Product Officer at Innodata, Rahul drives the innovation behind the leading Ai-based platforms that handle end-to-end data solutioning for some of the largest companies in the world. Prior to joining Innodata, he was Chief Product Officer at Equals 3, an AI marketing platform which won several accolades including Gartner Cool Vendor, CES Top 5, and IBM Watson ISV award. Before Equals 3, Rahul spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data and language. During his tenure at Watson, he grew usage of the services by over 100X and launched over 15 new services. Rahul is also an Adjunct professor at New York University (NYU) where he teaches Competitive Strategy and Advanced Experimental Design and Machine Learning.

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