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VictoriaMetrics
Anomaly Detection:
Q1’24 updates
Fred Navruzov, Product Data Scientist
Daria Karavaieva, ML Engineer
Recap: What is anomaly detection?
Anomaly detection (AD hereinafter) refers to the task of identifying unusual
patterns that do not conform to expected behavior
Such “patterns” may introduce significant challenges and losses to your business,
if not properly and timely treated
Monitoring is a hard task, AD itself is a harder task, and AD for time series data -
metrics measured over time - is the hardest due to specific effects, like trends,
seasonality or different anomaly patterns
Recap: Why ML & AI for anomaly detection?
Challenges caused by time series nature of metrics, make simpler approaches, like
threshold-based alerting way less effective for complex patterns (seasonality,
collective anomalies, etc.)
Manual handling of complex metrics doesn’t scale with the growth of your data
Incident management becomes even more complicated
as the volume and complexity of the data increases
Why VictoriaMetrics Anomaly Detection?
● A service that is simple to set up and to debug
● Integrates well with VictoriaMetrics observability ecosystem
● Collective & Contextual anomalies support
(i.e. seasonality, metric interaction)
● Root Cause Analysis for faster and efficient
metric incidents drilldown
5 new releases were shipped (1.8.0 - 1.12.0) in Q1’24. The most notable features:
Flexible configs - now user can define in a single config file
● Multiple model types - combine what’s best for your data yet avoid redundant
resource usage
● Multiple schedulers to set up fit/infer routines of a model
● `queries` arg of a model to define the data it should be run on
● And everything in many-to-many relationship!
What’s new: Flexible Configs
Config section
example
What’s new: Flexible Configs
Model autotune (introduced in v1.12): reduce the cognitive load and let the data
speak for itself
● Select the model class of your choice and let it derive best params from the training data
● Define max anomaly percentage param to encounter in your data - and that’s all
What’s new: AutoTune
● Quickstart - minimalistic guide on how to set up and run
`vmanomaly` (Docker, Kubernetes)
● Model types - explanations and diagrams to understand specifics
of a lifecycle and find the best model for your use case
● AutoTuned model introduction - find out how to set-and-forget
the model of your choice to learn from your data
● VictoriaMetrics Anomaly Detection got its own feature page
What’s new: Docs & site updates
Set up, tweak and evaluate anomaly detection easier and faster to best accommodate
your data patterns!
What’s coming: GUI
● Convenient shipping of forthcoming preset assets is ready - run vmanomaly
with `preset: preset_name` and found the assets under /presets endpoint
● Tweaking is still in progress for node_exporter preset to bring the best value
What’s coming: Presets
Roadmap for 2024
● (Q2) Streaming models support: spend less resources, adapt faster, gradually update the
models after each infer on data. Overall performance optimization.
● (Q2) GUI: Deeper integration with anomaly detection service, “optimize” button to run
autotune, see detected anomalies and get the best params of a model according to your data
● (Q2) Node_exporter preset. Presets for common tasks, like “seasonal_weekly”, “testing”,
“autotuned_daily”. Spend less time setting up the service, receive appropriate results faster.
● (Q3-Q4) Root Cause Analysis: Drill down your incidents faster and more efficient. Finishing
transition from PoC to production.
Try Anomaly Detection
Being a part of Enterprise offering,
VictoriaMetrics Anomaly Detection is available for testing:
contact us or request a trial license here to give it a try!
Thank you!
Have questions left? Contact Us

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VictoriaMetrics Anomaly Detection Updates: Q1 2024

  • 1.
  • 2. VictoriaMetrics Anomaly Detection: Q1’24 updates Fred Navruzov, Product Data Scientist Daria Karavaieva, ML Engineer
  • 3. Recap: What is anomaly detection? Anomaly detection (AD hereinafter) refers to the task of identifying unusual patterns that do not conform to expected behavior Such “patterns” may introduce significant challenges and losses to your business, if not properly and timely treated Monitoring is a hard task, AD itself is a harder task, and AD for time series data - metrics measured over time - is the hardest due to specific effects, like trends, seasonality or different anomaly patterns
  • 4. Recap: Why ML & AI for anomaly detection? Challenges caused by time series nature of metrics, make simpler approaches, like threshold-based alerting way less effective for complex patterns (seasonality, collective anomalies, etc.) Manual handling of complex metrics doesn’t scale with the growth of your data Incident management becomes even more complicated as the volume and complexity of the data increases
  • 5. Why VictoriaMetrics Anomaly Detection? ● A service that is simple to set up and to debug ● Integrates well with VictoriaMetrics observability ecosystem ● Collective & Contextual anomalies support (i.e. seasonality, metric interaction) ● Root Cause Analysis for faster and efficient metric incidents drilldown
  • 6. 5 new releases were shipped (1.8.0 - 1.12.0) in Q1’24. The most notable features: Flexible configs - now user can define in a single config file ● Multiple model types - combine what’s best for your data yet avoid redundant resource usage ● Multiple schedulers to set up fit/infer routines of a model ● `queries` arg of a model to define the data it should be run on ● And everything in many-to-many relationship! What’s new: Flexible Configs
  • 8. Model autotune (introduced in v1.12): reduce the cognitive load and let the data speak for itself ● Select the model class of your choice and let it derive best params from the training data ● Define max anomaly percentage param to encounter in your data - and that’s all What’s new: AutoTune
  • 9. ● Quickstart - minimalistic guide on how to set up and run `vmanomaly` (Docker, Kubernetes) ● Model types - explanations and diagrams to understand specifics of a lifecycle and find the best model for your use case ● AutoTuned model introduction - find out how to set-and-forget the model of your choice to learn from your data ● VictoriaMetrics Anomaly Detection got its own feature page What’s new: Docs & site updates
  • 10. Set up, tweak and evaluate anomaly detection easier and faster to best accommodate your data patterns! What’s coming: GUI
  • 11. ● Convenient shipping of forthcoming preset assets is ready - run vmanomaly with `preset: preset_name` and found the assets under /presets endpoint ● Tweaking is still in progress for node_exporter preset to bring the best value What’s coming: Presets
  • 12. Roadmap for 2024 ● (Q2) Streaming models support: spend less resources, adapt faster, gradually update the models after each infer on data. Overall performance optimization. ● (Q2) GUI: Deeper integration with anomaly detection service, “optimize” button to run autotune, see detected anomalies and get the best params of a model according to your data ● (Q2) Node_exporter preset. Presets for common tasks, like “seasonal_weekly”, “testing”, “autotuned_daily”. Spend less time setting up the service, receive appropriate results faster. ● (Q3-Q4) Root Cause Analysis: Drill down your incidents faster and more efficient. Finishing transition from PoC to production.
  • 13. Try Anomaly Detection Being a part of Enterprise offering, VictoriaMetrics Anomaly Detection is available for testing: contact us or request a trial license here to give it a try!
  • 14. Thank you! Have questions left? Contact Us