Elastic ML anomaly detection use case live

• Configure the approved machine learning anomaly-detection job.
• Set analysis time ranges, bucket spans and influencer fields.
• Establish the agreed alert rule and notification channel.
• Validate detector execution against representative historical and live data.
• Apply defined tuning adjustments within the agreed boundary.
• Hand over the configuration documentation and verification evidence.

• Ensure suitable historical and live time-series data is ingested, indexed and trusted.
• Maintain appropriate Elastic licence entitlements supporting Machine Learning.
• Provide domain context regarding seasonal patterns, maintenance windows and expected anomalies.
• Grant required cluster administrative access to configure machine learning jobs.
• Designate an acceptance participant to review and approve detector findings.

The target behaviour, suitable data and acceptance scenario must be defined before activation. If underlying data is not ready, the request returns to Advisory or a prerequisite Data & Logging deliverable.

• Bespoke statistical or custom Python model development.
• Contractual guarantees of specific predictive or forecasting accuracy.
• Broad data engineering, pipeline restructuring or historical backfilling.
• Delivering multiple independent machine learning use cases in one request.
• Continuous model tuning or managed anomaly operations post-handover.

• 1. Qualify behaviour and data: Confirm data volume, seasonality patterns, target metrics and variant tier.
• 2. Configure job: Define detector functions, bucket spans, split fields and influencers.
• 3. Run against representative data: Execute the job across historical data to establish baselines.
• 4. Tune within scope: Adjust anomaly thresholds or apply custom rules to reduce false noise.
• 5. Verify anomaly and alert: Validate that known or simulated anomalies fire the agreed notification.
• 6. Complete: Export job configurations, compile verification evidence and close handover.

The approved job runs on the agreed data, representative anomalies or test conditions are verified, the alert path works and the customer accepts the result. Evidence delivered at sign-off includes:

• Machine learning job configuration export.
• Representative data analysis window records.
• Anomaly results chart and scored deviation findings.
• Alert execution record demonstrating notification delivery.
• Documented tuning notes, exceptions (if any) and customer acceptance.

This deliverable is available only to customers with Elastic Specialist Advisory. Once the Service Advisor and, where required, Delivery have qualified the Service Request, the applicable credits are allocated from the customer's available credit pool. Delivery credits represent the defined result, not consulting hours.

If initial execution reveals that data frequency is too sparse, field values fluctuate randomly without detectable seasonality, or the use case requires multiple independent models, Observata pauses the affected work. The Service Advisor and Delivery then requalify the request to another option, another deliverable or a larger governed engagement before additional scope is performed or additional credits are consumed.

• 1. Identify the metric or log stream with fluctuating patterns during your regular working session.
• 2. Your Service Advisor checks data density, validates licensing and confirms the variant tier.
• 3. The Service Request is activated, locking the model boundary and credits upfront.
• 4. Delivery configures the job, validates anomaly alerts and provides documented proof of operation.

• Service family: HYPR Serve
• Outcome ID: EO-MLAD
• Specification Version: 1.3
• Effective Date: 23 September 2026

Governing scope: Observata legal centre