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BRIDGEi2i Organizes AWS User Group Meetup on Computer Vision

bridgei2i

BRIDGEi2i is pleased to host Alex Smola – VP & Distinguished Scientist at AWS for an informative and hands-on learning session on Computer Vision GluconCV & D2L.ai BANGALORE, May 14, 2019. on 18th May 2019 at the BRIDGEi2i auditorium. Awards & Recognition News & Updates. www.BRIDGEi2i.com.

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Reimagining Possibilities & Demystifying Exponential Technologies: A Panel Discussion

bridgei2i

BRIDGEi2i played host to an engaging panel discussion with the authors of the Wiley Innovation Black Book on Exponential Technologies. BRIDGEi2i is a trusted partner for enabling AI for Digital Enterprises by leveraging Data Engineering, Advanced Analytics, proprietary AI accelerators and Consulting expertise. News & Updates.

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BRIDGEi2i Newsletter: Q2 2019 Recap

bridgei2i

We added many interesting stories and also had the chance to host some insightful Fireside Chats with stalwarts from our community. We hosted Alex Smola – VP & Distinguished Scientist at AWS for a hands-on learning session about Computer Vision. Our “Humans of analytics community” continues to grow in strength and reach!

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BRIDGEi2i Newsletter: July 2019 Recap

bridgei2i

We added many interesting stories and also had the chance to host some insightful Fireside Chats with stalwarts from our community. We hosted Alex Smola – VP & Distinguished Scientist at AWS for a hands-on learning session about Computer Vision. Our “Humans of analytics community” continues to grow in strength and reach!

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BRIDGEi2i Newsletter: Q1 2019 Recap

bridgei2i

BRIDGEi2i hosted a panel discussion on the real-world applications of exponential technologies, which was attended by a few authors of the Exponential Black Book. BRIDGEi2i is a trusted partner for enabling AI for Digital Enterprises by leveraging Data Engineering, Advanced Analytics, proprietary AI accelerators and Consulting expertise.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.