Remove 2018 Remove Azure Remove Cloud Data
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IBM and Microsoft partnership accelerates sustainable cloud modernization

IBM Journey to AI blog

IBM’s recommendations included API-specific improvements, bot UX optimization, workflow optimization, DevOps microservices and design consideration, and best practices for Azure manage services.

Azure 93
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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Data privacy and compliance : With data stored in the cloud, navigating complex data privacy regulations like GDPR and CCPA becomes essential. For instance, British Airways faced a fine of £183 million ($230 million) for a GDPR breach in 2018. Poor data integration can lead to inaccurate insights.

Analytics 203
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Behind the glory: the dark sides of AI models that big tech will not tell you.

Towards AI

Building natural language processing and computer vision models that run on the computational infrastructures of Amazon Web Services or Microsoft’s Azure is energy-intensive. The Myth of Clean Tech: Cloud Data Centers The data center has been a critical component of improvements in computing.

AI 98
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How to Optimize Power BI and Snowflake for Advanced Analytics

phData

One big issue that contributes to this resistance is that although Snowflake is a great cloud data warehousing platform, Microsoft has a data warehousing tool of its own called Synapse. In a perfect world, Microsoft would have clients push even more storage and compute to its Azure Synapse platform.

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Present and future of data cubes: an European EO perspective

Mlearning.ai

EO data is not yet a commodity and neither is environmental information, which has led to a fragmented data space defined by a seemingly endless production of new tools and services that can’t interoperate and aren’t accessible by people outside of the deep tech community ( read more ). 2018, July). Remote Sensing, 12(24), 4033.

AWS 98
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What Can AI Teach Us About Data Centers? Part 1: Overview and Technical Considerations

ODSC - Open Data Science

Co-location data centers: These are data centers that are owned and operated by third-party providers and are used to house the IT equipment of multiple organizations. Edge data centers: These are data centers that are located closer to the edge of the network, where data is generated and consumed, rather than in central locations.