Domino Data Lab

APRIL 3, 2019

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. In short, the virtuous cycle is growing.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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. Along with your database servers, you had, data warehousing and business intelligence. Those workflows would feedback into your business analytics.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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. A typical data pipeline for machine learning. Use ML to unlock new data types—e.g.,

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238