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. for DG adoption in the enterprise.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

CIO Business Intelligence

MARCH 9, 2022

If those in charge of managing the data lake don’t create precise processes and metadata for organizing data, the lake can quickly devolve into what’s come to be known as a “data swamp”—a data lake that makes it hard for users to locate data. . Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

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. Then things changed. You can take TensorFlow.js

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