Splunk Machine Learning Toolkit
The Splunk Machine Learning Toolkit App delivers new SPL commands, custom visualizations, assistants, and examples to explore a variety of ml concepts.
Each assistant includes end-to-end examples with datasets, plus the ability to apply the visualizations and SPL commands to your own data. You can inspect the assistant panels and underlying code to see how it all works.
Look for our ML Youtube Playlist for simple explanations of how to use MLTK and what it is for..
ML Cheat Sheet https://docs.splunk.com/images/3/3f/Splunk-MLTK-QuickRefGuide-2019-web.pdf
Assistants:
* Predict Numeric Fields (Linear Regression): e.g. predict median house values.
* Predict Categorical Fields (Logistic Regression): e.g. predict customer churn.
* Detect Numeric Outliers (distribution statistics): e.g. detect outliers in IT Ops data.
* Detect Categorical Outliers (probabilistic measures): e.g. detect outliers in diabetes patient records.
* Forecast Time Series: e.g. forecast data center growth and capacity planning.
* Cluster Numeric Events: e.g. Cluster Hard Drives by SMART Metrics
Smart Assistants (new assistants with revamped UI and better ml pipeline/experiment management):
*Smart Forecasting Assistant (provides enhanced time-series analysis for users with little to no SPL knowledge and leverages the StateSpaceForecasting algorithm): e.g. forecasting app logons with special days
Available on both on-premise and cloud.
Splunk App for Data Science and Deep Learning
The Splunk App for Data Science and Deep Learning (DSDL), formerly known as the Deep Learning Toolkit (DLTK), lets you integrate advanced custom machine learning and deep learning systems with the Splunk platform. The app extends the Splunk Machine Learning Toolkit (MLTK) with prebuilt Docker containers for TensorFlow, PyTorch, and a collection of data science, NLP, and classical machine learning libraries. When you use the predefined workflows of Jupyter Lab Notebooks, the app enables you to build, test, and operationalize customized models with the Splunk platform. You can leverage GPUs for compute-intense training tasks and deploy models on CPU or GPU enabled containers.
Splunk Community for MLTK Algorithms on GitHub
Check out our Open Source community on Github that lets you share your algorithms with the community of Splunk MLTK users or import one of the algorithms that have been shared by the community: https://github.com/splunk/mltk-algo-contrib
The GitHub repo algorithms are also available as an app which provides access to custom algorithms. Cloud customers can use GitHub algorithms via this app and need to create a support ticket to have this installed:https://splunkbase.splunk.com/app/4403/
Available on cloud and on-premise
For the Splunk Machine Learning Toolkit documention, see: http://docs.splunk.com/Documentation/MLApp/latest.
Datasets
This application may contain certain sample files and datasets, which are provided for your convenience only. Such files and datasets contain information and data compiled by third parties, and Splunk makes no representation or warranty that the data contained in such files and datasets are true, accurate, complete or sanitized.
Requirements
You must install the Python for Scientific Computing Add-on before installing the Machine Learning Toolkit. Please download and install the appropriate version here:
Installation
To install an app within Splunk Enterprise:
- Log into Splunk Enterprise.
- Next to the Apps menu, click the Manage Apps icon.
- Click Install app from file.
- In the Upload app dialog box, click Choose File.
- Locate the .tar.gz or .tar file you just downloaded, then click Open or Choose.
- Click Upload.
