Remove 2017 Remove Metadata Remove Modeling
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Gartner Magic Quadrant for Metadata Management Includes Alation

Alation

Gartner predicts that “By 2020, 50% of information governance initiatives will be enacted with policies based on metadata alone.”. Magic Quadrant for Metadata Management Solutions , Guido de Simoni and Roxane Edjlali, August 10, 2017. Metadata management no longer refers to a static technical repository.

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5 Hardware Accelerators Every Data Scientist Should Leverage

Smart Data Collective

Although SageMaker has become a popular hardware accelerator since it was launched in 2017, there are plenty of other overlooked hardware accelerators on the market. This feature helps automate many parts of the data preparation and data model development process. A data visualization interface known as SPSS Modeler. Neptune.ai.

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Bringing the National Museum of African American History and Culture to the world

CIO Business Intelligence

The startup focused on federal contracts and earned its first contract with the Secret Service in 2017. Fearless chose Gatsby, a JavaScript framework for fast web page creation, and Craft CMS, which provides a speedy yet robust reach into the museum’s sizable catalog of metadata stored in AWS S3. Today, it employs more than 200.

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AI in Analytics: The NLQ Use Case

Sisense

NLQ serves those users who are in a rush, or who lack the skills or permissions to model their data using visualization tools or code editors. Last, and still a very painful challenge for most users, is the familiarity with the underlying data and data model. when the user actually meant to compare between Q1 2018 to the whole of 2017?

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What are model governance and model operations?

O'Reilly on Data

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development. Model governance. Source: Ben Lorica.

Modeling 195
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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. Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. There are models everywhere.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Introduction. That’s impressive.

Metadata 105