machine_learning

* Description *
SPL Copilot for Splunk (beta) empowers users to search their data using plain English. Now you can write a description of what you want in plain English, and Copilot translates the request into query ideas that you can execute or build on, all within a familiar Splunk interface.  Under the hood, SPL Copilot for Splunk uses machine learning to translate plain English instructions into an executable SPL query. SPL Copilot for Splunk makes SPL more accessible and helps you answer questions about your data faster. 

* Setup *
See the Details tab for dependencies and setup instructions.
 
* Help *
While this app is not supported by Splunk, the development team can be reached at mlsupport@splunk.com or via the splunk-usergroups Slack in the #machinelearning channel. Responses are made on a best effort basis. Feedback is always welcome and appreciated! 

Learn more about the splunk-usergroups Slack here: https://docs.splunk.com/Documentation/Community/current/community/Chat#Join_us_on_Slack)

This beta release is solely for internal testing and evaluation and not for production use.

SPL Copilot for Splunk (beta)

* Description *
Smart Alerts Assistant for Splunk (beta) enables analysts to focus on the most important and highest-priority issues. Under the hood, Smart Alerts Assistant for Splunk captures user feedback to improve prioritization and uses machine learning to output a recommended, stack-ranked priority for each alert.  Smart Alerts Assistant for Splunk empowers customers to optimize their operations centers and focus on top priorities instead of spending time triaging alerts.

* Setup *
See the Details tab for dependencies and setup instructions.

* Help *
While this app is not supported by Splunk, the development team can be reached at mlsupport@splunk.com or via the splunk-usergroups Slack in the #machinelearning channel. Responses are made on a best effort basis. Feedback is always welcome and appreciated! 

Learn more about the splunk-usergroups Slack here: https://docs.splunk.com/Documentation/Community/current/community/Chat#Join_us_on_Slack)

This beta release is solely for internal testing and evaluation and not for production use.

Smart Alerts Assistant for Splunk (beta)

The Splunk App for Anomaly Detection finds anomalies in time series datasets and provides an end-to-end workflow to manage and operationalize anomaly detection tasks. The app detects seasonal patterns and finds anomalies in just a couple of clicks.


Using the app, you can create anomaly detection jobs, run these jobs on a regular cadence, view SPL queries, and create alerts. The app works with any time series dataset that can be ingested into the Splunk platform, provided the points in the time series are evenly spaced.


The app uses machine learning to detect seasonality in the data without user inputs, lowering the barriers to realizing value. The app also performs health diagnostics on the time series to check whether the dataset is suitable for anomaly detection using our algorithm. If the dataset is not fit for anomaly detection with our algorithm, we have included steps to preprocess the data in the docs. (We are working on supporting more time series out of the box without preprocessing steps for the next version).


When you create an anomaly detection job, the app generates an SPL query you can view and use elsewhere within Splunk.


Similar to other Splunk applications, the resource consumption of CPU and memory is commensurate with the size of the datasets that you use.


This app is available for both on-premise and cloud customers.

Splunk App for Anomaly Detection

Intelligent_Experiences

This add-on contains a Python interpreter bundled with the following scientific and machine learning libraries: numpy, scipy, pandas, scikit-learn, and statsmodels. With this add-on, you can import these powerful libraries in your own custom search commands, custom rest endpoints, modular inputs, and so forth.

This add-on is available for Linux (64-bit and 32-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.

Python for Scientific Computing (for Mac Intel)

This add-on contains a Python interpreter bundled with the following scientific and machine learning libraries: numpy, scipy, pandas, scikit-learn, and statsmodels. With this add-on, you can import these powerful libraries in your own custom search commands, custom rest endpoints, modular inputs, and so forth.

This add-on is available for Linux (64-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.

Python for Scientific Computing (for Linux 64-bit)

Python for Scientific Computing (for Windows 64-bit)

Python for Scientific Computing (for Linux 32-bit)

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

Splunk Machine Learning Toolkit

Foundational_Capabilities

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. DSDL 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. By using predefined workflows for rapid development with Jupyter Lab Notebooks, DSDL enables you to build, test,and operationalise your custom 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 deep learning and machine learning algorithms for classification, regression, forecasting, clustering, graph analytics, and NLP. Learn from these examples to tackle your advanced data science use cases in the areas of IT Operations, Security, Application Development, IoT, Business Analytics, and beyond.