In this contributed article, Mohammad Musa, Founder & CEO of Deepen AI, discusses how good data labeling leads to better results, whether it’s in autonomous cars, medical imaging, or any other industry where AI thrives. Done poorly, the entire system suffers. Inefficiencies and inaccuracies become inevitable, while major safety risks caused by poor labeling can derail an entire project.
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Industry Perspectives
Why Technology Should Take a Backseat for Data Projects to Succeed
In this special guest feature, Andrew Stevenson, CTO and Co-founder at Lenses.io, believes that without tooling, governance and visibility around open source technologies, applying them successfully at an enterprise level is very hard. Organizations need to be able to access the latest platforms, tools and training, so that they don’t have to re-create technology that already has been commoditized. In a data driven world we can take this a step further and enable companies to focus on data to provide secure, governed access, visibility with the auditing and compliance modern platforms need.
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Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance
This whitepaper from Imply Data Inc. introduces Apache Druid and explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they leveraged the same technology to create end-to-end real-time analytics architectures.
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