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"Today, about 7% of cattle that are conceived don’t survive until weaning. In other species, it's as high as 20%. That equates to several million premature fatalities every year in the US. We think we can make a small difference, we'd be saving hundreds of thousands of lives. We're doing it today for about 50+ different species and breeds, using SuperAnnotate."
“SuperAnnotate’s platform is incredibly robust and easy-to-use. Their Data Operations team is very thorough, proactive, easy to engage, and acts as a valuable extension of Motorola Solutions’ data operations.”
Gathr is a next-gen, cloud-native, fully-managed, no-code data pipeline platform. It’s the only all-in-one platform for all your data integration and engineering needs – batch and streaming ingestion, CDC, ETL, ELT, data preparation, machine learning, and analytics. The Spark-based platform brings unmatched speed, performance, and flexibility required to handle all types of data and analytics approaches, in ways that traditional ETL tools cannot.
NVIDIA Run:ai accelerates AI and machine learning operations by addressing key infrastructure challenges through dynamic resource allocation, comprehensive AI life-cycle support, and strategic resource management. By pooling resources across environments and utilizing advanced orchestration, NVIDIA Run:ai significantly enhances GPU efficiency and workload capacity. With support for public clouds, private clouds, hybrid environments, or on-premises data centers, NVIDIA Run:ai provides unparalleled flexibility and adaptability.
Snorkel AI started as a research project in the Stanford AI Lab in 2016, where they set out to explore a higher-level interface to machine learning through programmatically labeled and managed training data. From deploying early versions of Snorkel Flow's core technology with some of the world’s leading organizations, to empowering scientists, doctors, and journalists, they’ve seen firsthand how this approach democratizes and accelerates AI. Now, they’re working to bring their technology to everyone.
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