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Introduction to Power BI Datamarts

ODSC - Open Data Science

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows.

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How AI-powered claims processing creates new efficiencies in insurance

Snorkel AI

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training datamodels are likely to make errors and produce inaccurate results. Book a demo today.

AI 64
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article thumbnail

How AI-powered claims processing creates new efficiencies in insurance

Snorkel AI

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training datamodels are likely to make errors and produce inaccurate results. Book a demo today.

AI 59
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How AI-powered claims processing creates new efficiencies in insurance

Snorkel AI

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training datamodels are likely to make errors and produce inaccurate results. Book a demo today. See what Snorkel option is right for you.

AI 52
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MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? Adapted from the book Effective Data Science Infrastructure. Data is at the core of any ML project, so data infrastructure is a foundational concern.

ML 145
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Mastering Version Control for ML Models: Best Practices You Need to Know

DagsHub

Data can change a lot, models may also quickly evolve and dependencies become old-fashioned which makes it hard to maintain consistency or reproducibility. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code. or other dedicated backup servers.

ML 52
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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.