ODSC Europe 2018

Daily Session Schedule

Schedule Guide for Pass Holders

ODSC Talks/Workshop schedule includes Friday September 21st and Saturday September 22nd. It is available is to Silver, Gold, Platinum, Diamond, and Platinum Business Pass holders

Training/Workshop schedule includes Wednesday September 19th and Thursday September 20th. It is available to Training,  Gold ( Thurs Sept 20th only), Platinum, and Diamond pass holders

Accelerate AI schedule is for Wednesday September 19th. It is available to Accelerate AI, Platinum Business, and Diamond pass holders

Speaker and speaker schedule times are subject to change. 

More sessions added weekly

ODSC Talks/Workshops Days
-Friday September 21st
--Saturday September 22nd
ODSC Training/ Workshops Days
-Wednesday September 19th
--Thursday, September 20th
Accelerate AI Europe
--Wednesday, Sept 19th
-Friday September 21st
--Saturday September 22nd
-Wednesday September 19th
--Thursday, September 20th
--Wednesday, Sept 19th
09:00
ODSC Keynote
09:30
Deploying Large Spark Models and scoring in Real time at Scale

Open Source Data Science | Data Science at Scale | Intermediate | Talks

Full Details.

Subhojit Banerjee
DataScientist/Engineer | Schiphol
09:30
Optimizing the Open Source Roots of Data Science

DataOps | Open Source Data Science | All | Talks

Data science professionals are often seen as “data artisans” and they like to use their own brushes (R, Python, Python Notebooks) to create insights and actions. Most companies started their data science journey relying on a couple of data scientists. There is a new demand to manage collaboration, teams, model lifecycle, deployment, model accuracy, and governance. Learn how best to embrace open source while intersecting with the maturity arch of analytics and keeping your data artisans engaged and innovative. Full Details.

Shawn Rogers
Senior Director of Analytic Strategy | TIBCO
09:30
Racing an Autonomous Toy Car from Scratch

Deep Learning | Open Source Data Science | Intermediate-Advanced | Talks

A racing competition held in France asked players to design an autonomous toy car, piloted with deep learning models. The deep learning approach implies to have training examples to quantify the weighs of the architecture. I decided to adopt a simulation technique to generate my training examples. As a result, the car is autonomous without having ever driven on the race. I would like to share with you the general workflow, from scratch, ranging from hardware assembling to racing in production, via training models. Full Details.