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AI in Analytics: The NLQ Use Case

Sisense

When the app is first opened, the user may be searching for a specific song that was heard while passing by the neighborhood cafe, or the user may want to be surprised with, let’s say, a song from the new experimental album by a Yemen Reggae folk artist. when the user actually meant to compare between Q1 2018 to the whole of 2017?

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Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline?

Sanjeev Mohan

Fast forward to early 2017. Then in the middle of 2017, a realization set in that we were one year away from GDPR and needed to focus on data governance. GCP has gained acceptance for development and experimentation and more enterprise customers are putting it into production.

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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
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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data. Certainly not!

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Real-Real-World Programming with ChatGPT

O'Reilly on Data

To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.

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What Are ChatGPT and Its Friends?

O'Reilly on Data

All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means.

IT 260
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Why adopt a hybrid, multi-cloud strategy?

Cloudera

of application workloads were still on-premises in enterprise data centers; by the end of 2017, less than half (47.2%) were on-premises. For example, if you want to optimize for agility and experimentation, you probably will be better off doing so with an ephemeral public cloud infrastructure. Enterprises are moving to the cloud.