|Date: September 26th 2017|
|Location: McKinsey office, New York City|
|Time: 6 PM|
|Registration Link: Distributed Deep Learning with TensorFlow in Docker containers - New York City (NYC) Advanced Analytics Meetup (New Yor…|
MapR recently announced the MapR XD platform, a global data fabric. It is a logical continuation of Convergence, which merges into a single cluster all of the MapR platform's technology in distributed file system, NoSQL and Document DB as well as real time event streams (Kafka). The sum of these technologies is 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 the MapR Persistent Application Client Container (PACC) to demonstrate distributed Tensorflow 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 cleverly used to improve and accelerate critical parts of the typical enterprise machine learning project, focusing on data ingest/data cleaning, model/dataset version and production deployment.
Mathieu Dumoulin is a Data Engineer on the MapR Professional Services team, and 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 and Convergence for machine learning.
You can find his blog posts on these topics, as well as Spark performance tuning, CaffeOnSpark and others on the MapR blog
- Distributed Deep Learning with TensorFlow in Docker containers - New York City (NYC) Advanced Analytics Meetup (New Yor…