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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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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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

CIO Business Intelligence

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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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. Embedding: The retrieved data is encoded into embeddings that the LLM can interpret.

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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.

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Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software

DataKitchen

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software Lowering Serious Production Errors Key Benefit Errors in production can come from many sources – poor data, problems in the production process, being late, or infrastructure problems. Data errors can cause compliance risks.

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5 key metrics for IT success

CIO Business Intelligence

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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