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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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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Also, a data model that allows table truncations at a regular frequency (for example, every 15 seconds) to store only relevant data in tables can cause locking and performance issues. The second streaming data source constitutes metadata information about the call center organization and agents that gets refreshed throughout the day.

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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. There are more than 80 million pages with semantic, machine interpretable metadata , according to the Schema.org standard. Take this restaurant, for example.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists. The gist is, leveraging metadata about research datasets, projects, publications, etc.,