Remove Business Intelligence Remove Deep Learning Remove Interactive Remove Metadata
article thumbnail

Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

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.,

article thumbnail

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

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
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

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.

article thumbnail

The Cloud Connection: How Governance Supports Security

Alation

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. Supports the ability to interact with the actual data and perform analysis on it. On-premises business intelligence and databases.

article thumbnail

Data Science, Past & Future

Domino Data Lab

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