O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. Data Quality and Standardization.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

MARCH 31, 2020

You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Ontotext

MAY 22, 2023

Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. We use other deep learning techniques for such tasks. Long story short, MDM is mostly about data quality and precision (e.g.,

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

O'Reilly on Data

JUNE 19, 2019

A catalog of validation data sets and the accuracy measurements of stored models. Versioning (of models, feature vectors , data) and the ability to roll out, roll back, or have multiple live versions. Metadata and artifacts needed for a full audit trail.

Modeling 194

Modeling Machine Learning Testing Metrics 194

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. You can take TensorFlow.js You know what?

Data Science 109

Data Science Machine Learning Data Governance Modeling 109