article thumbnail

Types of Data Models: Conceptual, Logical & Physical

erwin

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose. Conceptual Data Models: High-level, static business structures and concepts. Logical Data Models: Entity types, data attributes and relationships between entities.

Modeling 143
article thumbnail

Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

15 Lessons from the Data Story Creative Process

Juice Analytics

The answer is The Data Story Creative Process (DSCP) workshop — a hands-on, case study-based learning event that teaches a framework for using data to drive informed action. We learned a lot from our workshop. Visualize for readability and shared meaning. Then you can use models to predict where the story is going to end up.

article thumbnail

How Dafiti made Amazon QuickSight its primary data visualization tool

AWS Big Data

Data and its various uses is increasingly evident in companies, and each professional has their preferences about which technologies to use to visualize data, which isn’t necessarily in line with the technological needs and infrastructure of a company. This is a guest post by Valdiney Gomes, Hélio Leal, and Flávia Lima from Dafiti.

article thumbnail

Football and Data Models: How Teams Collaborate for a Common Goal

erwin

How uniting a football team around a common goal is like uniting IT and business users collaborating over a data model Football is back, baby! Tech users can bring knowledge of data infrastructure and modeling techniques while business users are able to contribute their understanding of specific business processes and requirements.

article thumbnail

Reference guide to analyze transactional data in near-real time on AWS

AWS Big Data

As a NoSQL solution, DynamoDB is optimized for compute (as opposed to storage) and therefore the data needs to be modeled and served up to the application based on how the application needs it. QuickSight gives decision-makers the opportunity to explore and interpret information in an interactive visual environment.

article thumbnail

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139