Workshop Half Day (3 hours) Details

ML Summit Summit 2018
1. - 2. Oktober 2018 | Berlin
Das große Trainingsevent für Python Developer

Joerg Schad


01 Okt 2018
10:00 - 13:00
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Building and Operating an Open Source Data Science Platform

01 Okt 2018
10:00 - 13:00

Monday, 1. October 2018 | 10:00 - 13:00

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 in the overall deep learning pipeline. This workshop gives an overview into building a complete automated deep learning pipeline starting with exploratory analysis, over training, model storage, model serving, and monitoring and answer questions such as:

  • How can we enable data scientists to exploratively develop models? 
  • How to automatize distributed Training, Model Optimization and serving using CI/CD?
  • How can we easily deploy these distributed deep learning frameworks on any public or private infrastructure?
  • How can we manage multiple different deep learning frameworks on a single cluster, especially considering heterogeneous resources such as GPU?
  • How can we store and serve models at scale?
  • How can we monitor the entire pipeline and track performance of the deployed models?

The participants will build an end-to-end data analytics pipeline 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
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