Machine Learning Toolkit

Machine Learning Toolkit

In machine learning, examples use to help you make generalizations about the whole thing. These generalizations, called models, use to do a lot of different things. Like predicting the value of a field, predicting future values, finding patterns in data, and looking for anomalies in new data. Users can use the Machine Learning Toolkit (MLTK) to make, confirm, and manage. Use machine learning models through a guided user interface.


Machine Learning Toolkit features

In the Machine Learning Toolkit, you can use these tools:

A collection of different sample datasets to help people learn about machine learning. Each end-to-end example comes with a guided modeling Assistant that shows you how to do different types of machine learning analysis and prediction. As well as what the best results look like when you use your own data. Find Showcases by machine learning operation or industry. Then look through them to see the examples that best match your machine learning goals..

Modeling with help Assistants to help you with your data source, algorithm, and any other settings. You need to make that algorithm work the way you want it to. Assistants put all the parts of a machine learning pipeline that are being watched into one interface.. Each Assistant gives you a choice of algorithms to fit and apply a model, with visualizations to help you understand what you’re seeing. Assistants use your own data to make Splunk Search Processing Language (SPL) for you. They do this work for you. In order to find out more about Assistant options, click here.

All of these are available through the Python for Scientific Computing library. It has more than 300 popular open-source algorithms that can use for free. For a list of the algorithms that are available.

Machine learning commands can add to SPL search commands so that they can do things like fit models to data and apply them. There are also commands that can list, sum up, and delete learned models. To learn more about SPL search command extensions, click here.

Information graphics can use again and again to look at and analyze data in a specific way.


The different types of machine learning are showing in this text

Machine learning is using to help you make generalizations about the whole thing. You can use these generalizations, called models, to predict the value of a field/future value. It can also find patterns in data, and look for anomalies in new data. Data and examples are important for machine learning to work. Without them, it can’t do any work at all.

There are a lot of different types of machine learning, like:


It predicts a number based on many factors. It is a tool for making predictions. It’s possible to keep track of how much CPU time a computer is using and how many discs reads or writes it does. There are tools that can help you figure out how much power that machine will use now and in the future.


Classification modelling predicts a category or a class based on a lot of different things. A classification is an analytical tool that can help you predict what will happen next. For example, you might have information about how people use a website or a piece of software. You can use classification modelling to figure out if that customer is going to switch to a different service provider.


In forecasting, you look at a single value as it moves through time to see how it will change in the future. Forecasting looks at past measurements of a single value. It’ll then calculate profit per day or CPU usage per minute, to figure out what that value is going to be in the future. Sales results for the last five years might break down by quarter. Use forecast modelling to figure out how much sales will be in the next quarter.


Clustering is a way to group together data that is similar. As an example, you might want to group people together based on how much money they spend or how many items they buy at once. Cluster modelling is a way to group together the features you choose.

Detection of anomalies

You can use anomaly detection to find outliers in your data. It does this by making an assumption based on one of the machine learning types and comparing it to reality. When the discrepancies between the two values are large, an alert will go off.


In the Machine Learning Toolkit, there are a lot of different parts.

This toolkit is a number of layers, each of which helps you build a generalization from your data.

Python for Scientific computing library that works with Python.

You can use over 300 open-source algorithms with this app!

To build the MLTK, you need to use the Python for Scientific Computing Library. You’ll find the most popular machine learning library called sci-kit learn. Also, you can see other tools like NumPy and Statsmodels.



Extensibility means that you can use any algorithm, whether it’s proprietary or open-source.

The MLTK has an extensibility API that allows you to show the 300+ algorithms from the Python app as well as write your own algorithms. If you make your own algorithms, you can share them and use them in the Splunk Community for the MLTK on GitHub.

Commands in ML

New SPL commands let you fit, test, score, and make models work.

In the MLTK, you can use the PSC app to show you new SPL commands that let you do machine learning. They help you build (fit), test (apply) and check out (score) models and more.



Over 30 standard algorithms are available to help people learn both supervised and unsupervised.

The commands in SPL show different algorithms. A standard algorithm is one that uses a lot. There are more than 30 of them.


Interactive examples of how to use IT, security, business, and IoT in everyday situations.

The MLTK has a lot of interactive examples from different fields, like IoT and business analytics. This is the Showcase, and it’s where people show off their work. MLTK’s Showcase page is the first one that you see when you start it up. It shows different examples from start to finish, so you can see how they work.


Experiments and helpers

Model building, testing, and deployment are all done together for the same goal.

On top of the tools for machine learning, there are “assistants” that help you figure out how to use them. Assistants built on an Experiment Management Framework (EMF). They show you how to do certain analytics with your own data.



Learning how to work with the Machine Learning Toolkit

In addition, if you are new to MLTK, you may study the Showcase examples. It will provide interactive machine learning examples. This guides you through the full process for IT, security, business, and Internet of Things use cases.

Several distinct datasets assist new users in their exploration of machine learning principles.

All the examples are pre-populated into an Assistant. It will show you step-by-step how to execute different sorts of machine learning analyses and predictions. as well as what the optimal outcomes look like when you use your own data.


Machine Learning Toolkit Showcase

The Machine Learning Toolkit (MLTK) Showcase helps users explore machine learning concepts. Each end-to-end example is comprised of a pre-populated use case for the Machine Learning Toolkit and each of the guided modeling Assistants. Filter the available Showcases by machine learning operation or industry to see the examples that best match your machine learning goals.

Machine learning operation options include Predict Fields, Detect Outliers, Forecast Time Series, and Cluster Events.

The MLTK ships with all of the example datasets used in the Showcase. You can use these datasets to practice machine learning concepts, or to re-create the Showcase examples in your own instance before working with your own data.


Showcase examples

The Showcase contains the following examples, grouped here by Assistant type.

Predict Numeric Fields

Algorithm: Linear regression People can use this to figure out what a number will base on the values of other fields in the same event. A common use of these predictions is to look for anomalies: predictions that are very different from the real value may call anomalous.


Predict Categorical Fields

Algorithm: Logistic regression Use the values of other fields in that event to figure out what a categorical field will be. A common use of these predictions is to look for anomalies: predictions that are very different from the real value may call anomalous.

Smart Outlier Detection

Algorithm: Density Function Find outliers in numbers by following a step-by-step process that helps you use a density algorithm and segment data before you start looking for them.


Detect Numeric Outliers

Algorithm: Distribution statistics Find values that are very different from previous values.


Detect Categorical Outliers

Algorithm: Probabilistic measures Find events that have unusual values in them.


Smart Forecasting

Algorithm: State Space Forecast Use a step-by-step guided workflow to predict the future of numeric time-series data. You can bring in data from different sources and accounts. It could be for calendar-specific “special days” like holidays, company-specific event days, and so on.


Forecast Time Series

Algorithm: State-space method using Kalman filter A metric can use to predict what the next values will base on how the metric has changed in the past (numeric time series).

Forecast Time Series

Smart Clustering

Algorithm: K-means Cluster numbers by following a step-by-step guide.


Cluster Numeric Events

Algorithms: K-means, DBSCAN, Spectral Clustering, Birch Cluster events that have more than one numeric field into groups.



Computer programs can become more accurate at predicting what will happen if you use machine learning (ML). Machine learning is a process that can use to find new ways to solve problems in a wide range of fields. There are a lot of tools in machine learning that can help you train data without having to write a single line of code. Framework, platform, library, and interface are some of the types of tools.

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