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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

Or we create a data lake, which quickly degenerates to a data swamp. Contextual data understanding Data systems often cause major problems in manufacturing firms. IBM built a workforce advisor that uses summarization and contextual data understanding with intent detection and multi-modal interaction.

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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

Then the data is consumed by SaaS-based computational tools, but it still sits within our organization and sits within the controls of our cloud-based solutions.” Much of Regeneron’s data, of course, is confidential. For that reason, many of its data tools — and even its data lake — were built in-house using AWS. “We

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The Award Winning Formula: How Cloudera Empowered OCBC With Trusted Data To Unlock Business Value from AI

Cloudera

To keep pace as banking becomes increasingly digitized in Southeast Asia, OCBC was looking to utilize AI/ML to make more data-driven decisions to improve customer experience and mitigate risks. While these are great proof points to demonstrate how business value can be driven by AI/ML, this was only made possible with trusted data.

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Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. We must resort to black box testing —testing the behavior of the function with a wide range of inputs.

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