TDAN

AUGUST 6, 2019

Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities.

Deep Learning 81

Deep Learning Technology Data Architecture Metadata 81

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). Most of the data management moved to back-end servers, e.g., databases.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Domino Data Lab

JULY 22, 2019

But the business logic kept getting more and more progressively rolled back into the middle layer, also called application servers, web servers, later being called middleware. Then in the bottom tier, you had your data management, your back office, right? That was the origin of big data. Then things changed.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. It’s More Important to Know What Your Data Means Than Where It Is. Cloud governance.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

O'Reilly on Data

MAY 21, 2019

Here are some typical ways organizations begin using machine learning: Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application. Use ML to unlock new data types—e.g., A typical data pipeline for machine learning.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238