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Case study: Policy Enforcement Automation With Semantics

Ontotext

Application-centric approach In the application-centric approach to data, people create an application to solve their problems today. So, even if it solves a problem for a while, the rapidly increasing needs of product and business teams lead to the proliferation of such applications.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

The prospect of developing synthetic minds that can learn and solve complex problems promises to revolutionize and disrupt many industries as machine intelligence continues to assume tasks once thought the exclusive purview of human intelligence and cognitive abilities. Regardless, these are examples of narrow AI.

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Threads Dev Interview 2: @alexanderbellgram

Data Science 101

NET framework to solve customer problems.” So, first thing for someone starting out: go to GitHub and find some projects that use the “good first issue” tag on some of their issues. Read blogs about tech and development. that east coast software teams use MS products and west coast teams prefer to avoid them.

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How Knowledge Graphs Power Data Mesh and Data Fabric

Ontotext

The problem is not the data silos, but the disconnect they cause. Enough about challenges and problems, let’s see how one can solve these challenges. For example, a product data tag is basic metadata. Product tags have barcodes, with numbers and symbols.

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“Without Data, Nothing” — Building Apps That Last With Data

Sisense

Yik Yak was an anonymous chat app that looked promising initially but failed because of problems that could have been resolved with data and analytics. Yik Yak could have tagged message content with the originating IP address and then quickly blocked messages from that IP after abusive language was detected.

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Keys to Ensure that Data isn’t Slowing Down your Innovation Efforts

Cloudera

Before training can even begin, the hard problem is collecting the labeled data that is crucial for training an accurate AI model,” said Joshua Robinson , a founding engineer of Pure Storage’s FlashBlade. It makes more sense to analyze and derive insights from it, and then place it in the data lake — properly tagged for easy access later.

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Fraud Detection with Cloudera Stream Processing Part 1

Cloudera

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. This is what we call the first-mile problem. This blog will be published in two parts.