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New Format for The Bar Chart Reference Page

The Data Visualisation Catalogue

To demonstrate the potential new content structure being implemented on an existing visualisation reference page, here’s an example provided for Bar Charts : Bar Chart. Journal of Experimental Psychology: Applied, 4 (2), 119–138. Other names: Bar Graph, Bar Plot. Functions: Comparisons, Rankings Encodings: Length. Description.

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 364
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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. Referring to the data dictionary and screenshots, its evident that the complete data lineage information is highly dispersed, spread across 29 lineage diagrams. where(outV().as('a')),

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

What this meant was the emergence of a new stack for ML-powered app development, often referred to as MLOps. ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs).

Testing 174
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise.

IT 364
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What you need to know about product management for AI

O'Reilly on Data

There may even be someone on your team who built a personalized video recommender before and can help scope and estimate the project requirements using that past experience as a point of reference. It’s difficult to be experimental when your business is built on long-term relationships with customers who often dictate what they want.

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Liberty Mutual CIO Monica Caldas on developing a digital-savvy workforce

CIO Business Intelligence

This initiative offers a safe environment for learning and experimentation. Phase two focused on developing use cases, creating a backlog, exploring domains for resource allocation, and identifying the right subject matter experts for testing and experimentation. We’ve structured our approach into phases.

Insurance 120