Open Source Summit + ELC Europe 2018

There are many great tutorials for training your deep learning models using TensorFlow, Keras, Spark or one of the many other frameworks. But training is only a small part of the overall deep learning pipeline.

Ever wonder about how to set up a complete end-to-end data science pipeline starting with data storage and preparation, interactive notebooks, distributed training, CI/CD automation, and serving and monitoring the trained models.

In this workshop, we will build an end-to-end OSS data science platform including:
* Data preparation using Apache Spark
* JupyterLab self-service for data scientists
* Data storage using HDFS
* Distributed training
* Automation & CI/CD using Jenkins
* Resource sharing (including GPUs) between multiple user/jobs
* Model and metadata storage
* Model serving and monitoring