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Accelerating Industry 4.0 at warp speed: The role of GenAI at the factory edge

CIO

Let’s take a look at how this could unfold over the next few years. implementations that strive to create plant-wide, fleet-wide, and enterprise-wide visibility, insights, and improvements. Manufacturers have been using gateways to work around these legacy silos with IoT platforms to collect and consolidate all operational data.

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AI and generative AI are revolutionizing manufacturing…here’s how

CIO

Manufacturers are attaining significant advancements in productivity, quality, and effectiveness with early use cases of AI and GenAI. AI can help with all of these challenges via manufacturing-specific use cases that benefit manufacturers, their employees, and their customers. Here’s how. Consider quality control.

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Infographic: How Can Data Quality Be Improved?

Dataiku

Data needs to be valuable (high quality, labeled, and organized) to drive machine learning model success. This infographic reveals some of the challenges data leaders face when it comes to data quality as well as a specific focus on the need for data labeling through active learning.

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5 key metrics for IT success

CIO

There are several important metrics that can be used to achieve IT success, says Jonathan Nikols, senior vice president of global enterprise sales for the Americas at Verizon. “To Failing isn’t as critical when your IT department is going to quickly and constantly change and improve.”

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4 Data-Driven Steps To Drive Successful B2B Demand Generation

Fact: Good data lives at the core of every successful B2B demand generation strategy. Without quality data, it’s nearly impossible to identify and segment your target audience and create messaging that speaks to their values and interests. Leverage intent data. Personalize messages to your priority accounts.

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8 data strategy mistakes to avoid

CIO

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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What LinkedIn learned leveraging LLMs for its billion users

CIO

Few companies operate at the scale that LinkedIn does or have access to similar troves of data. CIOs in every vertical can take a tip or two from the lessons LinkedIn learned along the way. LinkedIn declined to comment on how many Premium members it has.) But speed can be defined in multiple ways.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.