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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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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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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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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