What is ML Audit Trail?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

Record events across five lifecycle stages: data provenance (data sources accessed, transformations applied, filtering criteria, timestamps for each pipeline run), training activities (experiment configurations, hyperparameters, random seeds, training duration, compute resources consumed, evaluation metrics at each checkpoint), model management (registry actions including registration, promotion, approval/rejection with approver identity, deployment target assignments), production operations (deployment events, traffic routing changes, rollback triggers, configuration modifications), and prediction serving (sampled prediction logs with input features, model outputs, confidence scores, and model version identifier). Store immutable logs in append-only storage (AWS CloudTrail, Azure Immutable Blob, or custom write-once databases). Retain for the compliance period required by your industry, typically 3-7 years.

Use a three-tier logging strategy: high-frequency events (individual predictions) logged in compact binary format (Protobuf or Avro) with sampling at 1-10% for low-risk models and 100% for high-risk models, reducing storage by 90%. Medium-frequency events (training runs, deployments) logged in full detail to structured storage (PostgreSQL or BigQuery). Low-frequency events (policy changes, access control modifications) logged with full context to immutable audit stores. Implement asynchronous logging for prediction events using message queues (Kafka) to add zero latency to the serving path. Set retention policies: 90-day hot storage for operational queries, 1-year warm storage for investigations, and 3-7 year cold storage (S3 Glacier) for compliance. Total cost typically runs $200-1,000/month depending on prediction volume.

Record events across five lifecycle stages: data provenance (data sources accessed, transformations applied, filtering criteria, timestamps for each pipeline run), training activities (experiment configurations, hyperparameters, random seeds, training duration, compute resources consumed, evaluation metrics at each checkpoint), model management (registry actions including registration, promotion, approval/rejection with approver identity, deployment target assignments), production operations (deployment events, traffic routing changes, rollback triggers, configuration modifications), and prediction serving (sampled prediction logs with input features, model outputs, confidence scores, and model version identifier). Store immutable logs in append-only storage (AWS CloudTrail, Azure Immutable Blob, or custom write-once databases). Retain for the compliance period required by your industry, typically 3-7 years.

Use a three-tier logging strategy: high-frequency events (individual predictions) logged in compact binary format (Protobuf or Avro) with sampling at 1-10% for low-risk models and 100% for high-risk models, reducing storage by 90%. Medium-frequency events (training runs, deployments) logged in full detail to structured storage (PostgreSQL or BigQuery). Low-frequency events (policy changes, access control modifications) logged with full context to immutable audit stores. Implement asynchronous logging for prediction events using message queues (Kafka) to add zero latency to the serving path. Set retention policies: 90-day hot storage for operational queries, 1-year warm storage for investigations, and 3-7 year cold storage (S3 Glacier) for compliance. Total cost typically runs $200-1,000/month depending on prediction volume.

Record events across five lifecycle stages: data provenance (data sources accessed, transformations applied, filtering criteria, timestamps for each pipeline run), training activities (experiment configurations, hyperparameters, random seeds, training duration, compute resources consumed, evaluation metrics at each checkpoint), model management (registry actions including registration, promotion, approval/rejection with approver identity, deployment target assignments), production operations (deployment events, traffic routing changes, rollback triggers, configuration modifications), and prediction serving (sampled prediction logs with input features, model outputs, confidence scores, and model version identifier). Store immutable logs in append-only storage (AWS CloudTrail, Azure Immutable Blob, or custom write-once databases). Retain for the compliance period required by your industry, typically 3-7 years.

Use a three-tier logging strategy: high-frequency events (individual predictions) logged in compact binary format (Protobuf or Avro) with sampling at 1-10% for low-risk models and 100% for high-risk models, reducing storage by 90%. Medium-frequency events (training runs, deployments) logged in full detail to structured storage (PostgreSQL or BigQuery). Low-frequency events (policy changes, access control modifications) logged with full context to immutable audit stores. Implement asynchronous logging for prediction events using message queues (Kafka) to add zero latency to the serving path. Set retention policies: 90-day hot storage for operational queries, 1-year warm storage for investigations, and 3-7 year cold storage (S3 Glacier) for compliance. Total cost typically runs $200-1,000/month depending on prediction volume.