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Becoming a true data-driven organization requires adopting a more centralized approach to data architecture and analysis
Apache's streaming data processing system takes on Spark with better performance and more convenient debugging features
Search gives people the superpower to find an answer to almost any question. Machine learning is about to give that same capability to machines
How can we ensure that autonomous devices, including Internet of things endpoints, will never go rogue? Start with these three basic principles
The cloud makes dynamic-learning systems available and affordable -- but there aren't too many use cases for the technology
Reap the performance and developer productivity advantages of Spark for batch processing, streaming analysis, machine learning, and structured queries
Thanks to an impressive grab bag of improvements in version 2.0, Spark's quasi-streaming solution has become more powerful and easier to manage
Built on Watson and SPSS predictive analytics, IBM's cloud machine learning services address the needs of developers, data scientists, and businesses
When the cost of an error isn’t trivial, we need better approaches to error mitigation to operationalize and scale machine learning
The purchase will extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform