Strata + Hadoop World in San Jose 2017
Mar
14
Mar 17

Strata + Hadoop World in San Jose 2017

Join Open Data's founder and Chief Data Scientist, Bob Grossman, for his talk, The Dangers of Statistical Significance When Studying Weak Effects in Big Data: From Natural Experiments to p-Hacking. 

To learn more, please view his talk summary here


Predictive Analytics World New York
Oct
23
Oct 27

Predictive Analytics World New York

  • Jacob K. Javits Convention Center

ODG founder and Chief Data Scientist Bob Grossman details case studies concerning best practices and mistakes concerning deploying Analytic Models. 

Seattle Meet Up with Galvanize
Oct
13
6:00 pm18:00

Seattle Meet Up with Galvanize

  • Galvainze Seattle

We will be hosting a meet-up in Seattle in partnership with Galvanize.  Our CTO, Stu Bailey, will join a great group from the Data Science Seattle Meetup to discuss "AnalyticOps and Analytic Engines: Roles, Tools and Techniques for Deploying Analytic Models into Production in Modern Environments."

Here is a brief abstract of the talk.  We hope to see you there!

Stu will discuss three case studies to introduce available tools, hands on technical considerations, and best practices for deploying and future proofing analytic models into modern products, services and operational systems. He will discuss technical details and trends in data science requirements in relationship to technical details and trends developing in modern infrastructure solutions. He will conclude with concrete actions and pointers that will help attendees get hands on with the current state of the art technology as quickly as possible.

Stu Bailey at PAPI'16 Conference
Oct
11
Oct 12

Stu Bailey at PAPI'16 Conference

  • Microsoft New England Research and Development Center (N.E.R.D.), 1st Floor

Our CTO Stu will be attending and presenting at the PAPI'16 conference in October.  The title of his talk is Prediction | Production: Lessons from ‘Over-the-wall’, and is a talk centered on what it really means to be ready to deploy analytics.  The concept of an analytic deployment environment will be discussed, in addition to the organization competencies required to fully monetize data science investments.  The abstract is below, andif you are in Boston, we hope to see you there.

Data Science and Dev Ops teams live on opposite sides of a wall in most organizations. Despite the separation, these teams should work together to develop a coherent process to release analytic products, support those products and maintain sanity. We propose an institutional capability, ‘Analytic Operations’, to support data-driven processes within lines-of-business. We hope to share lessons learned practicing Analytic Ops and present a set of best practices for Analytic Ops teams. We also demo open source tools that reduce frictions between Data Science and Ops/Deployment teams.

The Data Science Conference
Oct
6
Oct 7

The Data Science Conference

  • Hyatt at Olive 8

Join ODG founder Bob Grossman in a discussion about a new functional role, AnalyticOps. 

Washington DC, Data Science Meet-up
Sep
29
6:00 pm18:00

Washington DC, Data Science Meet-up

Join Stu Bailey and the Statistical Programming DC community, part of the Data Community DC, as he discusses: AnalyticOps and Analytic Engines: Roles, Tools and Techniques

Stu will discuss three case studies to introduce available tools, hands on technical considerations, and best practices for deploying and future proofing analytic models into modern products, services and operational systems. He will discuss technical details and trends in data science requirements in relationship to technical details and trends developing in modern infrastructure solutions. He will conclude with concrete actions and pointers that will help attendees get hands on with the current state of the art technology as quickly as possible.

CIO Solutions Gallery Showcase 2016
Aug
31
Sep 1

CIO Solutions Gallery Showcase 2016

  • Blackwell Inn & Conference Center

Connect with us at Columbus for Bob Grossman's work on AnalyticOps, a key role in model deployment.