Remove 2017 Remove Analytics Remove Experimentation Remove Metadata
article thumbnail

AI in Analytics: The NLQ Use Case

Sisense

In my previous blog , I wrote about Natural Language Query (NLQ, or search analytics for some), as one of the major topics that we, the AI group in Sisense, are working on. NLQ is one of the oldest AI disciplines, but we’ve only recently started hearing about it in conjunction with BI and analytics. Machine Intent vs. User Intent.

article thumbnail

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

Sanjeev Mohan

Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline? Fast forward to early 2017. I saw the winds change and the inquiry requests shifted towards advanced analytics involving machine learning (ML) questions. The post Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline? I am glad you asked.

Insiders

Sign Up for our Newsletter

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

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
article thumbnail

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!

article thumbnail

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 262
article thumbnail

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. An integrated suite of data management and analytics tools in a single platform enables cost-effective delivery of complex, multiple use cases and thus reduces overall TCO. In 2016, 60.9%