Telecom

Network anomaly detection, churn prevention, and capacity planning

Detect network anomalies in real time, predict subscriber churn, and optimize capacity planning — with automated model lifecycle management and drift-triggered retraining.

The Challenges

Network Scale

Millions of events per second across cell towers, switches, and subscriber sessions require scalable anomaly detection.

Subscriber Churn

Predicting which customers will leave requires combining usage patterns, billing data, and customer service interactions.

Capacity Forecasting

Network expansion decisions rely on accurate traffic growth predictions across geographic regions and time horizons.

Data Silos

Network, billing, CRM, and support data live in separate systems with different schemas and update frequencies.

How CorePlexML Helps

AutoML

Anomaly Detection Models

AutoML trains models that identify network degradation patterns, DDoS signatures, and equipment failures from historical data.

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MLOps

Churn Prevention Pipeline

MLOps orchestrates the full lifecycle — from churn model training to production scoring with auto-retraining on subscriber drift.

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ML Studio

Capacity What-If Analysis

ML Studio lets planners test growth scenarios and see predicted congestion before committing to infrastructure investments.

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Dataset Builder

Cross-System Data Fusion

Dataset Builder merges network, billing, and CRM data through AI-guided joins, cleaning, and feature engineering.

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SDK Example

example.py

from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Train churn prediction model
experiment = client.experiments.create(
    project_id="proj_churn",
    dataset_version_id="dsv_subscriber_360",
    target_column="churned",
    max_models=25,
    max_runtime_secs=600
)

# Deploy with auto-retraining
deployment = client.deployments.create(
    project_id="proj_churn",
    model_id=experiment["leader_model_id"],
    strategy="canary",
    traffic_percentage=15
)

# Configure drift-triggered retraining
client.retraining.create_policy(
    deployment_id=deployment["id"],
    trigger="drift",
    threshold=0.05
)

Expected Impact

< 30s

Anomaly Detection Speed

0.91

Churn Prediction AUC

93%

Capacity Forecast Accuracy

70% faster

Data Integration Time

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

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