[[ml-gs-next]]
=== Next Steps

By completing this tutorial, you've learned how you can detect anomalous
behavior in a simple set of sample data. You created single and multi-metric
jobs in {kib}, which creates and opens jobs and creates and starts {dfeeds} for
you under the covers. You examined the results of the {ml} analysis in the
**Single Metric Viewer** and **Anomaly Explorer** in {kib}. You also
extrapolated the future behavior of a job by creating a forecast.

If you want to learn about advanced job options, you might be interested in
the following video tutorial:
https://www.elastic.co/videos/machine-learning-lab-3-detect-outliers-in-a-population[Machine Learning Lab 3 - Detect Outliers in a Population].

If you intend to use {ml} APIs in your applications, a good next step might be
to learn about the APIs by retrieving information about these sample jobs.
For example, the following APIs retrieve information about the jobs and {dfeeds}.

[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors

GET _xpack/ml/datafeeds
--------------------------------------------------
// CONSOLE

For more information about the {ml} APIs, see <<ml-api-quickref>>.

Ultimately, the next step is to start applying {ml} to your own data.
As mentioned in <<ml-gs-data>>, there are three things to consider when you're
thinking about where {ml} will be most impactful:

. It must be time series data.
. It should be information that contains key performance indicators for the
health, security, or success of your business or system. The better you know the
data, the quicker you will be able to create jobs that generate useful
insights.
. Ideally, the data is located in {es} and you can therefore create a {dfeed}
that retrieves data in real time.  If your data is outside of {es}, you
cannot use {kib} to create your jobs and you cannot use {dfeeds}. Machine
learning analysis is still possible, however, by using APIs to create and manage
jobs and to post data to them.

Once you have decided which data to analyze, you can start considering which
analysis functions you want to use. For more information, see <<ml-functions>>.

In general, it is a good idea to start with single metric jobs for your
key performance indicators. After you examine these simple analysis results,
you will have a better idea of what the influencers might be. You can create
multi-metric jobs and split the data or create more complex analysis functions
as necessary. For examples of more complicated configuration options, see
<<ml-configuring>>.

If you encounter problems, we're here to help. See <<xpack-help>> and
<<ml-troubleshooting>>.
