October 28–31, 2019

Deep Learning Toolkit for Splunk

Moderated by: Philipp Drieger (Staff Machine Learning Architect), Iman Makaremi (Principal Product Manager)


The Deep Learning Toolkit for Splunk allows you to integrate advanced custom machine learning systems with the Splunk platform. It extends Splunk’s Machine Learning Toolkit with prebuilt Docker containers for TensorFlow 2.0, PyTorch and a collection of NLP libraries. By using predefined workflows for rapid development with Jupyter Lab Notebooks the app enables you to build, test (e.g. using TensorBoard) and operationalise your models with Splunk. You can leverage GPUs for compute intense training tasks and flexibly deploy models on CPU or GPU enabled containers. The app ships with various examples that showcase different machine learning tasks like classification, regression, forecasting, clustering and NLP. This allows you to tackle advanced machine learning use cases in Splunk’s main areas of IT Operations, Security, IoT, Business Analytics and beyond.

  • O'Reilly
  • TensorFlow
  • Google Cloud
  • IBM
  • Databricks
  • Tensor Networks
  • VMware
  • Amazon Web Services
  • One Convergence
  • Quantiphi
  • Lambda Labs
  • Tech Mahindra
  • cnvrg.io
  • Determined AI
  • Inferencery
  • Manceps, Inc.
  • PerceptiLabs
  • Valohai

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