Remove Data Quality Remove Deep Learning Remove Modeling Remove Structured Data
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AI Adoption in the Enterprise 2021

O'Reilly on Data

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. It’s gratifying to note that organizations starting to realize the importance of data quality (18%).

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The Rise of Unstructured Data

Cloudera

In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. The challenges of data. Data annotation. Data curation.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio. 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.

Data Lake 115
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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

The first survey started as a simple exploration into mainstream adoption of machine learning (ML). What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production?