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Event: August 5, 2017
Big Data Day LA
Hear Alpine Data's VP of Engineering, Lawrence Spracklen, speak about operationalizing data science.
Recent Event: February 9 , 2017
Spark Summit East 2017
Hear Alpine's VP of Engineering speak about Spark Autotuning in Chorus.
News: June 28, 2018
(Re)defining Enterprise Data Science
There’s tremendous excitement around data science – maybe too much. We’re at the point in the hype cycle where executives are waking up to the fact that they’ve spent serious money on people, tools and technology… with minimal ROI to show for it...
In the News
Gartner Names Alpine Data Labs a Visionary in 2015 Magic Quadrant for Advanced Analytics Platforms
February 20, 2015
Alpine Data Labs Moves to “Visionaries Quadrant” Based on Completeness of Vision and Ability to Execute
The Future Of Money, Among Other Things….
San Francisco, CA Forbes 12 Jan, 2015
When Marc Andreessen wrote his famous “Software is eating the world” memo a few years ago, people might not have understood how fast the technology industry would progress.
In 2015, enterprises can better utilize the paradigm-shifting Hadoop
San Francisco, CA VentureBeat 3 Jan, 2015
In the coming year, I expect — or hope — that enterprises will realize that Hadoop is not simply a new database, or simply the latest advance in data processing and everyday analytics.
Hadoop Needs to Grow Up
San Francisco, CA ITBusinessEdge.com 1 Jan, 2015
We’re not even one week into 2015, and already Hadoop is being called out for needing to grow up.
Big Shots Of Big Data
San Francisco, CA VentureBeat 1 Jan, 2015
These days, companies don’t just have one data scientist hanging around. They have two or three or maybe even 150.
Alpine Enables Predictive Analytics for the Rest of Us
December 9, 2014
Chorus from Alpine Data Labs is a predictive analytics tool designed for data scientists and business analysts.
The Siren Song of Hadoop
With deep learning, the data-rich get richer
From presentation to conversation: How A.I. will transform enterprise apps
Machine learning: The deplorable state of deployment
Machine learning: Demystifying linear regression and feature selection
The cost of an error: Balancing the role of humans and machines