January 18, 2020

216 words 2 mins read

Economic Gains from Enterprise IT DevOps, Data Analytics and Open Source

Economic Gains from Enterprise IT DevOps, Data Analytics and Open Source

Enterprise adopters of IoT and Big Data have achieved significant gains when they apply new analytics to their operations; gains likely to be magnified as firms adopt intelligent analytics tied to AI …
Talk Title Economic Gains from Enterprise IT DevOps, Data Analytics and Open Source
Speakers Robert Cohen (Sr Fellow, Economic Strategy Institute)
Conference Open Networking Summit North America
Conf Tag
Location Los Angeles, CA, USA
Date Mar 26-30, 2018
URL Talk Page
Slides Talk Slides
Video

Enterprise adopters of IoT and Big Data have achieved significant gains when they apply new analytics to their operations; gains likely to be magnified as firms adopt intelligent analytics tied to AI and ML. The talk provides CXOs and IT architects with a new perspective for examining the potential economic benefits of DevOps and Open Source when combined with IoT, AI, ML and intelligent analytics. The talk uses case studies to explore new trends such as “Live Factories,” zero-downtime guarantees and “generalized” service monitoring. Many of the cases include quantified estimates of ROI when these transformative technologies are adopted. The talk will also explore differences in “success factors” as industries move to adopt the new technologies. It will identify the role Open Source is playing in determining the success of firms that are trying to become “Front Runners.”

intel ml data analytics open source analytics ai big data iot monitoring devops
comments powered by Disqus
Panel: Open Networking Driving Data Center and Cloud Innovation

Panel: Open Networking Driving Data Center and Cloud Innovation

January 14, 2020

Open networking encourages innovation through collaboration. Open specifications and the reference code also allow vendors to leverage external networking competencies to formulate and commercialize n …
Using machine learning to drive intelligence at the edge

Using machine learning to drive intelligence at the edge

January 16, 2020

The focus on the IoT is turning increasingly to the edge, and the way to make the edge more intelligent is by building machine learning models in the cloud and pushing them back out to the edge. Dave Shuman and Bryan Dean explain how Cloudera and Red Hat executed this architecture at one of Europe's leading manufacturers, along with a demo highlighting this architecture.
Hit a home run making baseball decisions using artificial intelligence and machine learning

Hit a home run making baseball decisions using artificial intelligence and machine learning

January 12, 2020

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and moreusing real-life baseball data to illustrate the concepts.
Meet Hedley, an AI, Linux, and smart sensor robotic skull

Meet Hedley, an AI, Linux, and smart sensor robotic skull

December 28, 2019

Rob Reilly explains how he brought Hedley, his robotic skull, to life. Hedley uses a JeVois smart machine vision sensor and artificial intelligence algorithms (developed by Laurent Itti) to track subjects as they move around in the skull's field of view. Come meet Hedley and learn about the latest developments in open source sensors, AI algorithms, and Linux-based physical computing.
StreamDM: Advanced data science with Spark Streaming

StreamDM: Advanced data science with Spark Streaming

December 5, 2019

Heitor Murilo Gomes and Albert Bifet offer an overview of StreamDM, a real-time analytics open source software library built on top of Spark Streaming, developed at Huawei's Noahs Ark Lab and Tlcom ParisTech.
Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling

Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling

November 30, 2019

The BigDL framework scales deep learning for large datasets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. Shivaram Venkataraman and Sergey Ermolin outline a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception and VGG.