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What a training plan is

A training plan is a packaged answer to one question — is this RADIUS session unusual for its APN?, is this part defective? — built and validated by the platform team and published in a catalogue. It bundles:

  • the algorithm and the whole training recipe: how the data is cleaned, split, transformed, trained and evaluated;
  • the container image that runs that recipe;
  • the data it needs: which source types it accepts (a file, a time series, or either) and, for time series, the columns it expects to find;
  • the configuration you must supply when you create a trainer — an APN, for example;
  • the minimum amount of data below which a training is refused rather than producing a meaningless model;
  • and, implicitly, the inference contract of the model it produces and the rule it creates to call it.

You do not modify a plan. You create a trainer that runs it, and the plan’s own pages below tell you what to feed it and what comes out.

Listing the catalogue

curl --request GET \
     --header "X-ApiKey: <your-api-key>" \
     https://api.opengate.es/ai/trainingPlans

The catalogue is platform-wide, not per organization, and read-only through the API. Each entry:

{
  "identifier": "6f1c2a4e-…",
  "name": "RADIUS anomalies per APN (Isolation Forest)",
  "description": "Detects anomalous data sessions of the subscriptions of one APN",
  "modelType": "anomaly",
  "modelFormat": "isolation-forest",
  "source": ["file", "timeserie"],
  "configFields": ["apn"],
  "columnData": [
    { "name": "APN", "type": "STRING", "description": "Access point name of the session" },
    { "name": "sbytes", "type": "LONG", "description": "Bytes sent by the device" }
  ],
  "minDataToTrain": { "value": 15000, "unit": "ITEMS" },
  "image": { "name": "trainingplan-if-radius-anomalies-per-apn", "tag": "1.0.0" }
}
Field Meaning How you use it
identifier The plan’s id imageExecution.trainingPlan.identifier when creating a trainer
name, description What the plan does, for people The console shows them in the plan picker
modelType The family of problem: anomaly, classification… Groups plans in the console
modelFormat The algorithm: isolation-forest, autoencoder, pytorch… Informative; it also names the registered model
source The data source types the plan accepts: file, timeserie or both Decides whether source.path or source.timeserie is allowed in the trainer
configFields The configuration keys the plan needs Every one of them must appear in imageExecution.configuration
columnData The columns the plan expects in its input For a time-series source, map each of them to a column of your time series
minDataToTrain The minimum amount of data: a number of ITEMS (rows, images per class) or of DAYS Below it, the training fails with an explicit message instead of producing a bad model
image The container image and tag the scheduler runs Informative

The plans available

  • RADIUS session anomalies — Isolation Forest

    Learns what a normal data session looks like on one APN and flags the sessions that do not fit: the data it needs, how it trains, the prediction request and answer, and the rule and alarm it creates.

  • RADIUS session anomalies — Autoencoder

    The same RADIUS-per-APN problem solved with a neural network that learns to reconstruct normal sessions and flags the ones it reconstructs badly: data, training, prediction contract and the rule it creates.

  • Image anomaly detection

    Tells defective items from correct ones in photographs and draws a heat map of the defect: the folders of images it trains on, the two models it combines, the prediction request over the organization's file space, and the rule and alarm it creates.

The three plans share the same lifecycle, described in How it works: what differs between them is the data they take, the request their inferencer answers, and the rule they create.

RADIUS · Isolation Forest RADIUS · Autoencoder Image anomaly detection
Source types file, time series file, time series file (a folder)
Input RADIUS session records of one APN RADIUS session records of one APN Photos in correct/ and incorrect/ folders
Configuration apn apn —
Inference request sbytes, dbytes, spkts, dpkts, dur sbytes, dbytes, spkts, dpkts, dur image_route, generate_heat_map
Inference answer prediction, anomaly_score, explanation prediction, anomaly_score, explanation predictions, anomaly_score, heatmap_path
Rule triggers on GPRS presence turning STOP GPRS presence turning STOP A new imagePathToCheck value
Alarm deviceWithAnomaly deviceWithAnomaly imageWithAnomaly

API specification