add note on readme on how to easily see all ml related blog posts (#14675)

1 files changed, 1 insertions, 0 deletions

diff --git a/ml/README.md b/ml/README.md
index 1acd457252..f8cb2fefc1 100644
--- a/ml/README.md
+++ b/ml/README.md
@@ -268,3 +268,4 @@ 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)