Adding upstream version 1.39.0.upstream/1.39.0

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 10 insertions, 3 deletions

diff --git a/ml/README.md b/ml/README.md
index 7f3ed276b..ac7c7c013 100644
--- a/ml/README.md
+++ b/ml/README.md
@@ -5,18 +5,22 @@ description: "This is an in-depth look at how Netdata uses ML to detect anomalie
 sidebar_label: "Configure machine learning (ML) powered anomaly detection"
 learn_status: "Published"
 learn_topic_type: "Tasks"
-learn_rel_path: "Setup"
+learn_rel_path: "Configuration"
 -->
 
 # Machine learning (ML) powered anomaly detection
 
 ## Overview
 
+Machine learning is a subfield of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. In monitoring, machine learning can be used to detect patterns and anomalies in large datasets, enabling users to identify potential issues before they become critical. The importance of machine learning in monitoring lies in its ability to analyze vast amounts of data in real-time and provide actionable insights that can help optimize system performance and prevent downtime. Machine learning can also improve the efficiency and scalability of monitoring systems, enabling organizations to monitor complex infrastructures and applications with ease.
+
+The primary goal of implementing machine learning features in Netdata is to enable users to detect and alert on anomalies in their systems with advanced anomaly detection capabilities. Netdata's machine learning features are designed to be highly customizable and scalable, so users can tailor the ML models and training process to their specific requirements and monitor systems of any size or complexity.
+
 As of [`v1.32.0`](https://github.com/netdata/netdata/releases/tag/v1.32.0), Netdata comes with ML powered [anomaly detection](https://en.wikipedia.org/wiki/Anomaly_detection) capabilities built into it and available to use out of the box, with zero configuration required (ML was enabled by default in `v1.35.0-29-nightly` in [this PR](https://github.com/netdata/netdata/pull/13158), previously it required a one line config change).
 
 🚧 **Note**: If you would like to get involved and help us with some feedback, email us at analytics-ml-team@netdata.cloud, comment on the [beta launch post](https://community.netdata.cloud/t/anomaly-advisor-beta-launch/2717) in the Netdata community, or come join us in the [🤖-ml-powered-monitoring](https://discord.gg/4eRSEUpJnc) channel of the Netdata discord.
 
-Once ML is enabled, Netdata will begin training a model for each dimension. By default this model is a [k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) model trained on the most recent 4 hours of data. Rather than just using the most recent value of each raw metric, the model works on a preprocessed ["feature vector"](#feature-vector) of recent smoothed and differenced values. This should enable the model to detect a wider range of potentially anomalous patterns in recent observations as opposed to just point anomalies like big spikes or drops. ([This infographic](https://user-images.githubusercontent.com/2178292/144414415-275a3477-5b47-43d6-8959-509eb48ebb20.png) shows some different types of anomalies.) 
+Once ML is enabled, Netdata will begin training a model for each dimension. By default this model is a [k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) model trained on the most recent 4 hours of data. Rather than just using the most recent value of each raw metric, the model works on a preprocessed ["feature vector"](#feature-vector) of recent smoothed and differenced values. This enables the model to detect a wider range of potentially anomalous patterns in recent observations as opposed to just point anomalies like big spikes or drops. ([This infographic](https://user-images.githubusercontent.com/2178292/144414415-275a3477-5b47-43d6-8959-509eb48ebb20.png) shows some different types of anomalies). 
 
 The sections below will introduce some of the main concepts: 
 - anomaly bit
@@ -114,7 +118,9 @@ To enable or disable anomaly detection:
 2. In the `[ml]` section, set `enabled = yes` to enable or `enabled = no` to disable.
 3. Restart netdata (typically `sudo systemctl restart netdata`).
 
-**Note**: If you would like to learn more about configuring Netdata please see [the configuration guide](https://github.com/netdata/netdata/blob/master/docs/guides/step-by-step/step-04.md).
+> 📑 Note
+> 
+> If you would like to learn more about configuring Netdata please see the [Configuration section](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md) of our documentation.
 
 Below is a list of all the available configuration params and their default values.
 
@@ -266,3 +272,4 @@ The anomaly rate across all dimensions of a node.
 - You should benchmark Netdata resource usage before and after enabling ML. Typical overhead ranges from 1-2% additional CPU at most.
 - The "anomaly bit" has been implemented to be a building block to underpin many more ML based use cases that we plan to deliver soon.
 - At its core Netdata uses an approach and problem formulation very similar to the Netdata python [anomalies collector](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/anomalies/README.md), just implemented in a much much more efficient and scalable way in the agent in c++. So if you would like to learn more about the approach and are familiar with Python that is a useful resource to explore, as is the corresponding [deep dive tutorial](https://nbviewer.org/github/netdata/community/blob/main/netdata-agent-api/netdata-pandas/anomalies_collector_deepdive.ipynb) where the default model used is PCA instead of K-Means but the overall approach and formulation is similar.
+- Check out our ML related blog posts over at [https://blog.netdata.cloud](https://blog.netdata.cloud/tags/machine-learning)