Deep Learning Toolkit for Splunk
The Deep Learning Toolkit for Splunk allows you to integrate advanced custom machine learning systems with the Splunk platform using TensorFlow 2.0, PyTorch and NLP libraries. Jupyter Lab Notebooks are providing data scientists and machine learning developers with an integrated experience from rapid prototyping to operationalising models in production. The app is freely available on splunkbase.
| Talk Title | Deep Learning Toolkit for Splunk |
| Speakers | |
| Conference | O’Reilly TensorFlow World |
| Conf Tag | |
| Location | Santa Clara, California |
| Date | October 28-31, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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.
