#Deploy TrustyAI Service

TrustyAI Service (TAS) deploys alongside KServe models and collects their inference data, which is what drift detection and bias metrics are computed from. This page creates the TrustyAIService resource; for ingesting data and registering metrics against it, see Bias and Drift Monitoring in the Monitor chapter.

#Prerequisites

• TrustyAI Operator installed (see Install TrustyAI).
• If storage.format: DATABASE is used, a MySQL 8.x database is required.
• If storage.format: PVC is used, ensure the cluster has a working default StorageClass (backed by a CSI driver) for dynamic volume provisioning, so the operator-created PVC can be bound.

#Deploy TrustyAIService

Choose one storage layout: DATABASE (MySQL) or PVC (local file storage on a volume). The following two subsections are alternatives, not steps in a single flow.

#DATABASE mode

MySQL credentials Secret

Create a secret that contains the keys required for the TAS deployment when using storage.format: DATABASE:

apiVersion: v1
kind: Secret
metadata:
  name: <tas-name>-db-credentials
  namespace: <your-namespace>
type: Opaque
stringData:
  databaseKind: mysql
  databaseUsername: <username>
  databasePassword: <password>
  databaseService: <mysql-service-name>
  databasePort: "3306"
  databaseName: <db-name>
  # Database schema generation strategy used by TrustyAI when connecting to the database.
  # It controls what TAS does to the database schema (tables) during startup:
  # - none: do not manage schema
  # - create: create schema from scratch
  # - drop-and-create: drop existing schema, then create
  # - drop: drop existing schema
  # - update: update schema to match the expected model
  # - validate: only validate that the schema matches the expected model
  #
  # Default: update
  databaseGeneration: update

Notes:

• The MySQL schema (database) referred to by databaseName must be created in advance. TAS does not create the database itself.
• The database must be reachable from the TAS pod.
• databaseGeneration controls how schema changes are handled when TAS starts.

TrustyAIService CR

Example:

apiVersion: trustyai.opendatahub.io/v1
kind: TrustyAIService
metadata:
  name: <tas-name>
  namespace: <your-namespace>
  annotations:
    trustyai.cpaas.io/monitor-enable: "true"
    trustyai.cpaas.io/monitor-interval: "30s"
    trustyai.cpaas.io/monitor-metric-regex: "^trustyai_.*"
spec:
  storage:
    format: DATABASE
    databaseConfigurations: <tas-name>-db-credentials
  metrics:
    schedule: "5s"
    batchSize: 5000
  replicas: 1

In DATABASE mode, storage.databaseConfigurations must be set to the name of the MySQL credentials Secret created above in the same namespace as the TrustyAIService.

metadata.annotations are optional and are used to let the operator create a ServiceMonitor for Prometheus scraping (so the platform can automatically collect monitoring data).

• When trustyai.cpaas.io/monitor-enable: "true" is set, the operator generates a ServiceMonitor.
• trustyai.cpaas.io/monitor-interval and trustyai.cpaas.io/monitor-metric-regex are optional; when not provided, the operator uses default values.

trustyai.cpaas.io/monitor-interval controls how frequently Prometheus scrapes TAS metrics (default: 30s). trustyai.cpaas.io/monitor-metric-regex controls which metric names are kept after scraping (default: ^trustyai_.*).

spec.metrics fields:

• schedule (required): how often TAS runs the metric computation (for example, every 5s). The value is a duration string.
• batchSize (optional): how many inference records TAS includes in each metric computation run (a larger value uses more data per run). If not set, the operator uses a default value of 5000.

#PVC mode

Use this path when storage.format is PVC (no MySQL Secret). Example:

apiVersion: trustyai.opendatahub.io/v1
kind: TrustyAIService
metadata:
  name: <tas-name>
  namespace: <your-namespace>
spec:
  storage:
    format: PVC
    folder: /inputs
    size: 1Gi
  data:
    filename: data.csv
    format: CSV
  metrics:
    schedule: "5s"
    batchSize: 5000
  replicas: 1

In PVC mode:

• storage.folder: the path inside the mounted PVC where TAS stores and reads its data.
• storage.size: the requested PVC capacity (for example, 1Gi).
• The operator creates a PVC named <tas-name>-pvc automatically in the same namespace as the TrustyAIService. Since the PVC uses the cluster default StorageClass (no explicit storageClassName is set), the cluster should provide a working default StorageClass (otherwise the PVC may stay in Pending).

When the TrustyAIService manifest for the chosen mode is ready, apply it (the same command applies to DATABASE or PVC YAML):

kubectl apply -f <trustyai-service>.yaml -n <your-namespace>

#Verify deployment readiness

kubectl get trustyaiservices -n <your-namespace> <tas-name>

The expected status.phase should be Ready.

Also check the pods:

kubectl get pods -n <your-namespace> -l app.kubernetes.io/instance=<tas-name>

#Next

With the service Ready, continue with Bias and Drift Monitoring to ingest reference and live inference data, register drift and bias metrics, and expose them to Prometheus.