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The growing role of data and machinelearning cuts across domains and industries. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machinelearning and AI). Data Science and MachineLearning sessions will cover tools, techniques, and case studies.
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Now more than ever, companies are looking for new ways to incorporate data analytics into their daily operations and leverage data-driven insights to improve business functions. The post Understanding Data Storage: Lakes vs. Warehouses appeared first on DevOps.com. However, understanding […].
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Bayer Crop Science has applied analytics and decision-support to every element of its business, including the creation of “virtual factories” to perform “what-if” analyses at its corn manufacturing sites. These systems integrate storage and processing technologies for document retrieval and analysis. Crop planning. Clinical DSS.
The underlying large-scale metrics storage technology they built was eventually open sourced as M3. It will give users more detailed notifications around workflows, with root cause analysis, and it will also give engineers, whether or not they are data science specialists, more tools to run analytics on their data sets.
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Those it does disclose on its site are smaller names like Kin, Beyond and Public Storage. The company has a number of large multinationals among its customers — it does not disclose which, but Jain cited a few major telecoms companies by name in our conversation, without confirming if they were actual customers. Observe.ai
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It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API. There are also newer AI/ML applications that need data storage, optimized for unstructured data using developer friendly paradigms like Python Boto API.
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