Machine Learning brings data analysis into a new era, allowing companies to use predictive analytics that continually “learn” from historical data. These analytics can optimize IT, security and business operations — helping to detect incidents, reduce resolution times, predict and prevent undesired outcomes.
The Splunk platform makes it easy for you to harness the power of machine learning by offering a rich set of commands and a guided workbench to create custom models for any use case.
Our Splunk Solution Center provides your teams with guidance and support for a successful integration of machine learning and analytics dedicated to managing IT services and security.
We help you test, implement and develop custom Splunk solutions for IT service intelligence, user behavior analytics, and many custom applications with use cases where machine learning can now be used.
By working in collaboration with our team, you will quickly get to harness the power of machine learning and exploit the machine learning commands the Splunk platform has to offer.
The Machine Learning Toolkit modeling environment offers a guided workbench to create custom models with minimal complexity.
Assistants let you choose the algorithm and then guide you through model creation, testing and deployment for common objectives like forecasting values, predicting numeric or categorical fields, and detecting numeric or categorical outliers.
Walk through interactive examples of model creation organized by common use cases for IT, security, IoT and business analytics. Examples include predicting disk failures, finding outliers in response time, predicting VPN usage and forecasting internet traffic.
SPL ML Commands
The Splunk platform offers over 20 machine learning commands that can be applied directly to your data for detection, alerting or analysis. Commands such as outlier, predict, cluster and correlate utilize fixed algorithms, while others such as anomaly detection allow you to choose between several algorithms to best fit your needs.
Want more flexibility?
With the Machine Learning Toolkit, you get access to additional commands and open source algorithms to create custom models for any use case.
Python for Scientific Computing Library
Use machine learning SPL commands like fit, apply and allow to directly build, test and operationalize models using Python algorithms from the Splunk Python for Scientific Computing Add-on.
Some examples of successes of operating Machine Learning models:
- Reducing customer service disruption with early identification of difficult to detect network incidents
- Minimizing cell tower degradation and downtime with improved issue detection sensitivity
- Speeding website problem resolution by automatically ranking priorities and identifying probably root cause for support engineers
- Ensuring mobile device security by detecting anomalies in ID authentication
- Improving uptime and lowering costs by predicting/preventing cell tower failures and optimizing repair truck rolls