AWS Big Data

JULY 26, 2023

This enables you to process a user’s query to find the closest vectors and combine them with additional metadata without relying on external data sources or additional application code to integrate the results. Carl has been with Amazon Elasticsearch Service since before it was launched in 2015.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Rita Sallam

APRIL 2, 2023

Interpretation of our machine learning model suggests buying $50k in building coverage and $100k in warehouse content coverage, as the latter significantly boosts predicted flood claim payouts. When exploring river and coastal flooding for the USA on a city level, river flooding area had a drastic increase starting from 2015.

Optimization 52

Optimization Machine Learning Insurance Risk 52

In(tegrate) the Clouds

DECEMBER 21, 2015

Merv Adrian (@merv) December 19, 2015. What is the most important news item about a software company that occurred in 2015 that belongs in the capsule, and why? The resurgence of Microsoft as a cloud company was big news in 2015. Who was the biggest tech disruptor in 2015? platform.twitter.com/widgets.js.

Technology 45

Technology Internet of Things Digital Transformation Software 45

Domino Data Lab

SEPTEMBER 9, 2019

We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. That nlp variable is now your gateway to all things spaCy and loaded with the en_core_web_sm small model for English. metadata=convention_df["speaker"]? ).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Answer: Better than every other vendor?

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

AWS Big Data

JUNE 6, 2023

The Data Science teams also use this data for churn prediction and CLTV modeling. Since we started exporting GA tracking data to BigQuery in 2015 the amount of data tracked and stored has grown 70x (logical bytes) and is >3TB in total. What we needed was a data pipeline from BigQuery to our Data Lake and the DWH.

Analytics 55

Analytics Data Lake Testing Optimization 55

Andrew White

JANUARY 9, 2022

A data fabric has the same model but it adds a set of capabilities that continuously monitor the whole landscape, and continuously update the intelligent part that infers insight as new data is discovered and/or added. I would suggest that one way to explore it is to explore the idea of a standard operating model.

Analytics 64

Analytics Measurement Modeling Data-driven 64