Please join us for an exciting evening to learn more aboutDistributed Deep Learning with TensorFlow in Docker containers from Mathieu Dumoulin, Data Engineer at MapR technologies.

RSVP Details:Youmust have your full nameas it appears on your ID on your meetup account to pass beyond building security. Guests must join the NYC Advanced Analytics Meetup.

Schedule:

6:00 pm - 6:20 pm: Networking, pizza & drinks

6:20 pm - 6:25 pm:Welcomeby McKinsey & Company

6:25 pm - 6:30 pm:Introby Jamal Syed from Hexstream

6:30 pm - 7:30 pm:Talkby Mathieu Dumoulin from MapR

7:30 pm - 8:00 pm: More networking!

Sponsors:

Hexstream is offering pizza & beverage.

McKinsey & Company is hosting the event.

Talk description:

MapR recently announced the MapR XD platform, a global data fabric. It is a logical continuation ofConvergence,which merges into a single cluster all oftheMapR platform's technology in distributed file system, NoSQL & Document DB as well as real time event streams (Kafka). The sum of thesetechnologiesis definitely greater than the parts.

In this talk, we'll look at convergence in action using distributed deep learning as an example. First, we're going to make use of theMapR Persistent Application Client Container (PACC)to demonstrate distributedTensorflow running within docker containers on data stored on MapR.

Then, using the Deep Learning example as a base, we'll show how the unique features of MapR can be cleverlyused to improveand accelerate critical parts of the typical enterprise machine learning project, focusing on data ingest/data cleaning, model/dataset version & production deployment.

Bio:

Mathieu Dumoulin is a Data Engineer on the MapR Professional Services team, & is based in Tokyo, Japan. Machine Learning at scale has been the major focus of his interest since he finished his Masters degree at Universite Laval in Quebec City in Canada in early 2010's.

Since joining MapR last year, Mathieu has been a frequent speaker at conferences like Strata on topics such as streaming architecture, real-time predictive maintenance & Convergence for machine learning. You can find his blog posts on these topics, as well as Spark performance tuning, CaffeOnSpark & others on the MapR blog