Machine Learning Toolkit
Tap into actionable insights with machine learning and guided assistance from Splunk
Apply machine learning to your data for actionable insights that inform faster, smarter decisions.
Family of smart assistants
Accelerate time to value with step-by-step guided workflows to build and deploy models for common business challenges.
Actionable intelligence
Operationalize your machine learning: Collect and analyze data, train models iteratively, and set alerts — in real time.
Accelerate ML use cases
Develop production models for outlier and anomaly detection, predictive analytics and clustering.
See What the MLTK Can Do For You
SPL & Open Source Library Integration
commands to directly build, test and operationalize supervised and unsupervised models. Access the TensorFlow™ library through the Splunk MLTK Container for TensorFlow™, available through certified Splunk Professional Services. Use any of the pre-packaged Python algorithms, or import any of 300+ open-source ones.
Faster Performance for Large Data Sets
Detect Numeric and Categorical Outliers
Time Series Forecasting
Cluster Numeric Events
Predict Numeric and Categorical Fields
CUSTOMER STORY
Hyatt Reduces MTTR and Gives Developers More Time to Innovate
Hyatt uses machine learning in Splunk Enterprise to predict when and where we should act fast or plan differently to best serve our customers... We’re using artificial intelligence through Splunk to more proactively serve our customers in this way everyday.
Through intensive training, AI and machine learning establish baselines for your data and detect deviations from past behavior or atypicalities that might otherwise go undetected. See how the National Ignition Facility identifies atypical behavior as it monitors the U.S. nuclear stockpile.
See the Unseen in Your Data With Clustering
There are patterns in your data that human analysts will miss: trends in ITOps and in security, and patterns in customer behaviors that suggest new markets and opportunities. Automate analysis of clusters to identify and group similar data points to help you see the signals in the noise, and make better decisions. See how pharma startup Recursion identifies high-value patterns in large sets of genetic research data.
