diff options
author | Andrew Maguire <andrewm4894@gmail.com> | 2023-03-07 10:29:17 +0000 |
---|---|---|
committer | GitHub <noreply@github.com> | 2023-03-07 12:29:17 +0200 |
commit | 1af0c0f144816f43d6f22b23da8d80be5e0f1f1c (patch) | |
tree | feaa8239c7d58444559687a76354919b6b762fec /ml/README.md | |
parent | ce2005b525cfd42bde1b9275eeb2db217351b5c4 (diff) |
add note on readme on how to easily see all ml related blog posts (#14675)
Diffstat (limited to 'ml/README.md')
-rw-r--r-- | ml/README.md | 1 |
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) |