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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

A good place to start is refreshing the way organizations govern data, particularly as it pertains to its usage in generative AI solutions. For example: Validating and creating data protection capabilities : Data platforms must be prepped for higher levels of protection and monitoring.

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How Can The Adoption of a Data Platform Simplify Data Governance For An Organization?

Pickl AI

Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance.

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Want to be a hybrid cloud winner? The recipe for XaaS success

IBM Journey to AI blog

Overcoming challenges means addressing data ingestion bottlenecks, hybrid cloud AI model distribution, robust model safeguarding through advanced encryption and governance for trustworthiness. The recipe for XaaS success appeared first on IBM Blog. Read the whitepaper on XaaS today The post Want to be a hybrid cloud winner?

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How IBM HR leverages IBM Watson® Knowledge Catalog to improve data quality and deliver superior talent insights

IBM Journey to AI blog

A long-standing partnership between IBM Human Resources and IBM Global Chief Data Office (GCDO) aided in the recent creation of Workforce 360 (Wf360), a workforce planning solution using IBM’s Cognitive Enterprise Data Platform (CEDP). Data quality is a key component for trusted talent insights.

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Improving air quality with generative AI

AWS Machine Learning Blog

The solution addressed in this blog solves Afri-SET’s challenge and was ranked as the top 3 winning solutions. This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors.

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Foundational models at the edge

IBM Journey to AI blog

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai