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Data architecture strategy for data quality

IBM Journey to AI blog

Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues. What does a modern data architecture do for your business? What does a modern data architecture do for your business?

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. Security SMEs review the architecture based on business security policies and needs.

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How to accelerate your data monetization strategy with data products and AI

IBM Journey to AI blog

Data monetization is a business capability where an organization can create and realize value from data and artificial intelligence (AI) assets. A value exchange system built on data products can drive business growth for your organization and gain competitive advantage. Creating value from data involves taking some action on the data.

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Unlocking financial benefits through data monetization

IBM Journey to AI blog

This value exchange system uses data products to enhance business performance, gain a competitive advantage, and address industry challenges in response to market demand. Cost optimization can be achieved through a combination of productivity enhancements, infrastructure savings and reductions in operating expenses.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale.

ML 101