## 🔍 PHASE 1 — ML Lifecycle & Lineage Context Reconstruction Objective: Identify every stage through which the churn prediction model passes, and determine what evidence must be captured. Proposed lifecycle 1\. Customer Data Sources Transactions, purchase history 2\. Dataset & Feature Engineering Snapshots, transformations, validation 3\. Training Execution Code, configuration, environment 4\. Model Artifact & Evaluation Model digest, metrics, approval evidence 5\. Registry & Deployment Approved artifact, package, deployment ID 6\. Runtime Verification & Monitoring Actual serving artifact, audit trail, rollback Lifecycle evidence table | Stage | Required evidence | Current test status | | ------------------- | ---------------------------------- | ------------------- | | Data acquisition | Source and extraction timestamp | Not supplied | | Dataset preparation | Snapshot ID and content digest | Not supplied | | Feature generation | Transformation version | Not supplied | | Training | Run ID and input references | Not supplied | | Evaluation | Artifact-linked metrics | Not supplied | | Registration | Registry ID and immutable digest | Not supplied | | Approval | Reviewer, decision, timestamp | Not supplied | | Deployment | Package and deployment IDs | Not supplied | | Runtime | Actual serving artifact identity | Not supplied | | Rollback | Restored artifact and verification | Not supplied | Result: The required lifecycle is defined, but actual lineage completeness cannot yet be established. ## 🧬 PHASE 2 — Model Artifact Identity & Versioning Objective: Ensure the system can distinguish two model artifacts even when they share the same name. Hypothetical model identity - Model name: `customer-churn-predictor` - Registry version: `v12` - Immutable artifact ID: `model-artifact-7f31` - Content digest: `sha256:<computed-digest>` - Training run ID: `train-run-204` - Creation timestamp: Must be recorded by the training pipeline. - Storage location: Must reference the actual artifact object. - Parent artifacts: Dataset snapshot, feature definitions, code revision, configuration. - Lifecycle status: Proposed, evaluated, approved, deployed, or retired. The identifiers above are illustrative except for the digest placeholder, which must be computed from actual content. Required controls - Every artifact receives a stable identifier. - The content digest is computed from the stored artifact bytes. - Registry versions are linked to immutable artifact identities. - A mutable tag such as `latest` is never treated as proof of identity. - Model weights, preprocessors, and other necessary package components are tracked. - Artifact replacement creates a new identity or a new content version with auditable history. Test case - Action: Upload two different model files under the same human-readable model name. - Expected result: The files are distinguishable by their immutable identifiers and content digests. - Failure condition: Both files appear identical in lineage records merely because their names match. Result: Identity design proposed; test execution not performed. ## 🔗 PHASE 3 — Provenance Graph & Lineage Relationships Objective: Connect every important artifact and lifecycle event in a verifiable graph. Proposed lineage chain Dataset Snapshot dataset-snapshot-88 Consumed by Training Run train-run-204 Generated Model Artifact model-artifact-7f31 Evaluated by Evaluation Record eval-record-62 Approved through Approval Record approval-record-19 Deployed as Production Deployment deployment-501 Required relationship metadata - Source node and destination node. - Relationship type. - Timestamp and event producer. - Evidence reference. - Verification status. - Integrity information where required. Validation scenarios - Trace backward from the production deployment to the dataset and source code. - Trace forward from a model artifact to every deployment that uses it. - Reject unsupported relationships inferred solely from matching names or timestamps. - Flag missing links rather than filling them with guesses. Result: Proposed graph is logically connected. The example IDs are placeholders, not actual records. ## 🗃️ PHASE 4 — Dataset & Feature Lineage Objective: Determine exactly which customer data and feature logic influenced the model. Required metadata - Dataset ID and immutable snapshot/version. - Source system and extraction time. - Transformation code revision. - Feature definition version. - Training, validation, and test split references. - Label-generation logic and relevant time window. - Schema and data-quality validation results. - Data access restrictions and retention rules. - Historical snapshot availability. Hypothetical example - Dataset: `customer-transactions` - Snapshot: `dataset-snapshot-88` - Feature pipeline: `feature-pipeline-v6` - Example features: days since last purchase, order frequency, and average order value. - Training linkage: must be explicitly recorded in `train-run-204`. Failure scenario: The original transaction table changes after training, but the pipeline only records the table name. Impact: The team cannot prove that a reconstructed dataset matches the original training input. Recommended remediation - Record immutable snapshots or content-addressed dataset references. - Preserve transformation versions and data split definitions. - Verify the availability and integrity of historical inputs. - Apply access controls to sensitive customer data. Result: Dataset lineage requirements identified; no real dataset or snapshot was supplied for verification. ## 💻 PHASE 5 — Source Code, Configuration & Environment Traceability Objective: Identify the exact code and environment responsible for generating the artifact. Required training record - Repository identifier. - Full commit hash. - Release or branch reference for context. - Training configuration version. - Dependency lockfile digest. - ML framework and runtime versions. - Container image digest. - Hardware and accelerator details where relevant. - Random seeds and relevant nondeterministic settings. - Execution parameters. Hypothetical example - Repository: `ml/customer-churn` - Commit: `<full-git-commit-hash>` - Configuration: `churn-training-config-v4` - Container: `sha256:<container-image-digest>` - Dependency lockfile: `sha256:<lockfile-digest>` Test case - Action: Attempt to explain the training environment using only a branch name and a mutable container tag. - Expected result: The lineage validator reports insufficient evidence for exact environment identification. - Remediation: Capture the full commit hash, immutable image digest, configuration, and dependencies. Important distinction: Exact provenance does not guarantee bit-for-bit reproduction. Hardware behavior, nondeterministic operations, and external dependencies may affect the result. Result: Traceability specification defined; the actual repository and environment remain unknown. ## 🧪 PHASE 6 — Training Run & Experiment Traceability Objective: Connect each training execution to its inputs, outputs, metrics, and status. Proposed training record | Field | Example | | --------------- | ------------------------------------------ | | Run ID | `train-run-204` | | Input snapshot | `dataset-snapshot-88` | | Feature version | `feature-pipeline-v6` | | Code revision | Full commit hash required | | Configuration | `churn-training-config-v4` | | Output artifact | `model-artifact-7f31` | | Run status | Unknown until recorded | | Metrics | Must be retrieved from actual run evidence | | Start/end time | Must be recorded | | Failure reason | Required for failed runs | Scenarios to handle - Failed training must not produce a release marked as validated. - Partial runs must be distinguishable from completed runs. - Resumed training must preserve parent-run and checkpoint relationships. - Hyperparameter searches must link candidate runs to their experiment. - Fine-tuning and model merging must record all parent artifacts. - Checkpoint selection must document why the selected checkpoint was used. Test case - Action: Simulate a failed training run that leaves a checkpoint file. - Expected result: The checkpoint is recorded as an output of an incomplete run and cannot bypass evaluation and approval controls. - Failure condition: The checkpoint is automatically promoted as a production-ready model. Result: Run-record structure proposed; no training execution has actually been tested. ## 📊 PHASE 7 — Evaluation, Validation & Approval Lineage Objective: Ensure approval applies to the exact artifact that was evaluated. Required evidence - Exact artifact ID and content digest. - Evaluation dataset snapshot. - Evaluation code and metric-definition versions. - Configuration and actual results. - Acceptance criteria. - Reviewer or authorized approver identity. - Decision timestamp and outcome. - Relevant robustness, fairness, leakage, and regression tests. Hypothetical evaluation record - Evaluation ID: `eval-record-62` - Artifact: `model-artifact-7f31` - Evaluation dataset: `churn-validation-snapshot-21` - Metric: F1 score, precision, recall, and PR-AUC where appropriate. - Acceptance threshold: Must be defined by the product team. - Approval: Not established without an actual decision record. Test case - Action: Replace the evaluated model file after approval. - Expected result: The digest mismatch invalidates the old evaluation-to-artifact match and triggers re-evaluation or explicit policy review. - Failure condition: The replacement silently inherits approval from the previous artifact. Result: Approval safeguards proposed; no evaluation result or approval is verified. ## 📦 PHASE 8 — Registry & Artifact Storage Architecture Objective: Prevent missing, overwritten, or unverified model artifacts. Proposed architecture components - Model registry: Stores model names, versions, lifecycle states, and links to artifact identities. - Artifact storage: Stores model weights, preprocessors, and deployment packages. - Metadata store: Stores lineage relationships and lifecycle events. - Experiment tracker: Connects experiments and training runs to their outputs. - Container registry: Stores versioned container images. - Audit log: Records registration, approval, replacement, and deployment events. Required controls - Immutable artifact references and content digests. - Restricted write permissions. - Verification of artifact content after upload. - Metadata completeness checks before registration. - Retention policies for historical artifacts. - Detection of broken references and unauthorized replacement. Test case - Action: Delete an artifact from storage while retaining its registry entry. - Expected result: A storage-integrity check reports the missing artifact, and deployment is blocked if the artifact cannot be verified. - Failure condition: The registry continues to present the artifact as available without detecting the missing content. Result: Storage and registry controls proposed; no real registry has been inspected. ## 🚀 PHASE 9 — Deployment & Runtime Traceability Objective: Verify which exact model artifact is actually serving production predictions. Required deployment chain - Approved model artifact. - Deployment package. - Deployment configuration. - Target environment. - Deployment execution ID. - Serving endpoint. - Runtime-reported active artifact identity. - Active traffic allocation, where applicable. Hypothetical deployment record | Field | Example | | ---------------------- | ----------------------------------------- | | Artifact ID | `model-artifact-7f31` | | Deployment ID | `deployment-501` | | Environment | Production | | Endpoint | Must be obtained from the real deployment | | Active artifact digest | Must be verified at runtime | | Traffic allocation | Must be observed | | Deployment status | Unknown until verified | Test case - Action: Request deployment of artifact A, but configure the serving service to load artifact B. - Expected result: Runtime verification detects that the actual loaded digest differs from the approved digest. - Required response: Alert the responsible team and prevent or halt the rollout according to deployment policy. Additional scenarios - Canary deployment with multiple active versions. - Blue/green deployment and traffic switching. - Shadow inference. - Batch inference using a specific artifact version. - Regional deployments using different model versions. Result: Deployment verification design proposed; no production endpoint or runtime evidence is available. ## 🔄 PHASE 10 — Model Promotion, Rollback & Replacement Lineage Objective: Maintain a defensible history of model promotion and rollback. Proposed promotion flow - Development - Evaluation - Staging - Approval - Production - Retirement Promotion requirements - Source artifact and destination lifecycle state. - Required evaluation evidence. - Approval identity and authorization. - Recorded decision and timestamp. - Failure conditions and policy checks. Hypothetical rollback scenario A newly deployed churn model produces an unacceptable increase in prediction errors. The team decides to restore a previously approved model. Required rollback record - Failed deployment: `deployment-501` - Previously active artifact: Must be retrieved from deployment history. - Rollback trigger: Documented incident or threshold breach. - Restored artifact: Exact immutable identity and digest. - Rollback action: Deployment event and timestamp. - Verification: Runtime-reported digest and health checks. - Final state: Actual serving artifact and traffic allocation. Critical distinction - Model rollback restores a previous model artifact. - Configuration rollback restores previous serving settings. - Feature-pipeline rollback restores prior feature logic. - Data rollback restores or references previous data state. - Infrastructure rollback restores relevant runtime infrastructure. Restoring an old model does not necessarily restore old behavior if the feature pipeline, input data, or serving configuration has changed. Result: Rollback workflow proposed; no rollback has been executed. ## 🔐 PHASE 11 — Provenance Integrity, Security & Access Control Objective: Protect artifacts and lineage records from unauthorized modification. Proposed permission boundaries | Role | Proposed permissions | | ---------------------- | ------------------------------------------------ | | Data pipeline service | Record authorized dataset and feature events | | Training service | Create runs and training artifacts | | Registry service | Register and manage version records | | Evaluator | Produce evaluation evidence | | Approver | Authorize promotion within assigned scope | | Deployment service | Deploy authorized artifacts | | Auditor | Read lineage and audit records | | Security administrator | Manage policies and investigate integrity events | Required controls - Least-privilege access. - Separate artifact creation from production approval. - Restrict direct modification of historical lineage records. - Maintain append-only or tamper-evident audit history where required. - Compute and verify artifact digests. - Use digital signatures and provenance attestations with an explicit trust policy. - Verify signer identity and authorization. - Protect credentials and sensitive dataset references. - Audit changes to permissions and approval policies. Test case - Action: An unauthorized identity attempts to change the digest recorded for an approved artifact. - Expected result: The modification is rejected or detected and recorded as a security event. - Failure condition: The lineage record changes without an attributable audit event. Result: Security controls proposed; enforcement has not been verified. ## 🧩 PHASE 12 — Lineage Gap & Reproducibility Analysis Objective: Identify the missing information that could prevent an audit or reconstruction. Hypothetical gap register | Gap | Impact | Priority | | -------------------------- | -------------------------------------------------- | -------- | | Missing dataset snapshot | Original training data may be unrecoverable | Critical | | Missing source commit | Training logic cannot be identified precisely | High | | Missing environment digest | Runtime dependencies may be uncertain | High | | Unlinked evaluation report | Approval evidence cannot be attributed confidently | Critical | | Missing artifact digest | Content identity cannot be verified | Critical | | Missing runtime identity | Actual serving model is unverified | Critical | | Missing rollback evidence | Restoration cannot be demonstrated | High | These are potential gaps to check, not confirmed defects in a real system. Priorities are provisional and should be validated against business impact. Reproducibility classifications - Traceable: Required lineage links are recorded and verifiable. - Partially traceable: Some required links are missing. - Reproducible under defined conditions: Required inputs and execution conditions are available, with stated limitations. - Reproducibility unverified: Evidence is insufficient to establish reproducibility. - Not reproducible with available evidence: Required historical inputs or execution details cannot be recovered. Recommended remediation order 1. Establish artifact identity and digest verification. 2. Link artifacts to training runs. 3. Capture immutable dataset and code references. 4. Attach evaluation and approval evidence. 5. Verify actual production runtime identity. 6. Validate rollback history and historical artifact availability. Result: Potential gaps and remediation paths identified. Actual gap severity requires system evidence. ## 📈 PHASE 13 — Observability, Audit & Incident Investigation Objective: Make lineage useful during production incidents and routine audits. Required query capabilities - Which artifact is serving at a given endpoint? - Which deployment introduced a particular model version? - Which training run produced the deployed artifact? - Which dataset and code versions influenced it? - Which evaluation and approval authorized deployment? - Which deployments use a particular artifact? - Which production models are affected by a newly discovered data or model issue? Proposed operational indicators - Percentage of production deployments with verified artifact identity. - Percentage of registered artifacts with complete required metadata. - Number of broken lineage references. - Number of unresolved digest mismatches. - Time required to trace a deployment back to its training inputs. - Number of unauthorized lineage modification attempts. - Percentage of rollback events with verified final runtime state. Test case - Action: Investigate a production incident involving unexpected churn predictions. - Expected result: Investigators can trace the serving artifact backward through deployment, approval, evaluation, training, code, and dataset records. - Failure condition: The investigation stops because a critical lineage relationship is missing. Important: Lineage-system health measures traceability. It does not prove prediction quality, model fairness, or business effectiveness. Result: Operational monitoring requirements defined; no actual dashboard or incident investigation has been tested. ## 🧪 PHASE 14 — Lineage Validation & Failure Testing Objective: Verify that the architecture detects incorrect, incomplete, or inconsistent lineage. Proposed test suite | Test | Expected result | | ----------------------------- | ------------------------------------------------ | | Training run outputs artifact | Output ID and digest are recorded | | Dataset linkage | Correct snapshot is linked to the run | | Artifact integrity | Computed digest matches recorded digest | | Evaluation linkage | Evaluation references the exact artifact | | Approval linkage | Approval references the correct evaluation | | Deployment linkage | Deployment references the approved artifact | | Runtime verification | Loaded artifact matches the approved digest | | Rollback verification | Restored artifact and final state are recorded | | Missing metadata | Promotion is blocked or explicitly flagged | | Unauthorized modification | Attempt is rejected or detected | | Artifact replacement | Stale attestations are invalidated or reverified | | Concurrent deployments | Lineage events remain correctly associated | Detailed sample test: Runtime Artifact Verification - Objective: Confirm that the production service loads the artifact authorized for deployment. - Preconditions: Approved artifact identity, expected digest, deployment record, and a mechanism to obtain the actual loaded artifact identity. - Procedure: 1. Retrieve the approved artifact ID and digest. 2. Deploy the approved package. 3. Query the running service's artifact identity. 4. Compare the actual digest with the approved digest. 5. Record the result and relevant evidence. - Expected result: The actual serving artifact matches the approved artifact. - Failure condition: The runtime digest is different, unavailable, or cannot be verified. - Evidence to retain: Expected and observed digests, deployment ID, timestamp, service identity, and verification outcome. Execution status: This is a proposed test procedure, not a test that has been run. No pass result can be claimed. ## 🗺️ PHASE 15 — Implementation Architecture & Roadmap Objective: Implement the lineage system without building unnecessary infrastructure. Proposed component responsibilities - Metadata collection: Capture dataset, code, configuration, and environment information. - Lineage event capture: Record artifact creation and lifecycle transitions. - Artifact identity service: Maintain immutable identifiers and content digests. - Relationship store: Preserve lineage links and support backward and forward traversal. - Registry integration: Link registry versions to immutable artifacts. - Training integration: Record run inputs, outputs, status, and diagnostics. - Evaluation integration: Bind reports and approvals to the exact artifact. - Deployment integration: Record package, environment, and deployment references. - Runtime verification: Confirm which artifact is actually loaded. - Integrity verification: Detect missing or altered content. - Audit logging: Preserve attributable change history. - Query interface: Support incident investigations and audits. - Monitoring and alerting: Detect incomplete lineage and integrity failures. Implementation roadmap 1. Milestone 1 — Artifact identity: Generate stable IDs and content digests for model artifacts. 2. Milestone 2 — Training provenance: Record immutable data, code, configuration, and environment references. 3. Milestone 3 — Run-to-artifact linkage: Capture training outputs and execution status. 4. Milestone 4 — Evaluation and approval: Bind validation evidence and decisions to exact artifacts. 5. Milestone 5 — Registry integration: Verify artifact references and metadata completeness. 6. Milestone 6 — Deployment traceability: Connect approved artifacts to deployment records. 7. Milestone 7 — Runtime verification: Compare actual loaded artifact identities against approved identities. 8. Milestone 8 — Integrity and security: Enforce permissions, digest checks, and auditable changes. 9. Milestone 9 — Rollback validation: Test artifact restoration and final runtime verification. 10. Milestone 10 — Gap monitoring: Alert on missing metadata, broken links, and unverified deployments. Recommended starting approach - Extend existing experiment tracking, registry, artifact storage, and CI/CD systems wherever possible. - Establish a common artifact identity and event schema. - Add promotion gates for critical missing lineage. - Prioritize production runtime verification before investing in a separate, large lineage platform. Result: A phased implementation strategy is defined. Actual timelines and technology choices require details about the existing stack and team capacity. ## 🏆 PHASE 16 — Final Model Artifact Lineage Blueprint ### 1. Executive Summary - System: E-commerce customer churn prediction. - Architecture objective: Trace the model from source data through training, evaluation, approval, deployment, and production runtime. - Current maturity: Cannot be determined because no real implementation records were provided. - Primary risks to investigate: Missing artifact identity, incomplete dataset lineage, detached evaluation evidence, and unverified runtime model identity. - Highest-priority recommendation: Establish immutable artifact identity and verify the model actually serving production predictions. ### 2. ML Lifecycle & Artifact Inventory - Dataset snapshots and transformation definitions. - Source code and training configurations. - Training runs and checkpoints. - Model weights and preprocessing artifacts. - Evaluation reports and approval records. - Registry entries and deployment packages. - Deployment manifests, runtime records, and rollback events. Required outcome: Every critical artifact has a stable identity and traceable relationships to its producers and consumers. ### 3. Artifact Identity & Versioning Model - Use immutable artifact identifiers. - Compute content digests for stored artifacts. - Track registry versions separately from content identity. - Preserve historical artifacts according to retention requirements. - Verify identity before evaluation, approval, and deployment. ### 4. Provenance Graph & Relationship Definitions - Connect datasets to training runs. - Connect code and configurations to training executions. - Connect training runs to model artifacts. - Connect artifacts to evaluation and approval records. - Connect approved artifacts to deployment and runtime records. - Preserve replacement and rollback history. ### 5. Dataset & Feature Lineage - Capture dataset snapshots and extraction timestamps. - Record transformation code and feature definitions. - Track labels, splits, schemas, and data-quality results. - Verify that historical training inputs remain available where required. ### 6. Code, Configuration & Environment Traceability - Record source commit hashes and dependency lockfiles. - Capture configuration versions and container image digests. - Record relevant hardware, runtime, and random-seed information. - Document limitations affecting reproducibility. ### 7. Training Run & Experiment Lineage - Assign a unique run ID. - Record inputs, outputs, status, metrics, and execution timestamps. - Link resumed training and fine-tuning to parent runs or artifacts. - Prevent failed or incomplete runs from bypassing release controls. ### 8. Evaluation & Approval Traceability - Link evaluation evidence to the exact artifact digest. - Preserve evaluation datasets, code, metrics, and acceptance criteria. - Record approval identity and decision timestamps. - Require renewed evaluation or approval when a material artifact change invalidates previous evidence. ### 9. Registry & Artifact Storage Architecture - Keep artifact storage distinct from lineage metadata. - Validate registry references and stored content. - Restrict write access and retain audit history. - Detect missing files, inconsistent metadata, and unauthorized replacement. ### 10. Deployment & Runtime Traceability - Link approved artifacts to deployment packages and execution records. - Record environment, serving configuration, and traffic allocation. - Verify the actual loaded artifact at runtime. - Alert on mismatches between approved and observed identities. ### 11. Promotion, Rollback & Replacement Lineage - Enforce documented promotion requirements. - Preserve the identity of the previously active artifact. - Record rollback triggers, actions, and verification evidence. - Validate model, configuration, feature, and data rollback independently where necessary. ### 12. Provenance Integrity & Access Controls - Apply least-privilege permissions. - Separate training, evaluation, approval, and deployment authority. - Verify artifact digests. - Protect historical lineage records with appropriate audit and tamper-detection controls. - Establish trust and verification policies for signatures and attestations. ### 13. Lineage Gap & Reproducibility Register - Investigate missing dataset snapshots. - Identify absent code and configuration references. - Find evaluation records without artifact links. - Detect missing artifact digests and runtime verification. - Assign remediation priorities based on operational and audit impact. ### 14. Validation & Failure Testing Plan - Test dataset-to-run linkage. - Test run-to-artifact linkage. - Verify digest consistency. - Test evaluation and approval association. - Verify deployment and runtime identity. - Test rollback, unauthorized changes, and concurrent deployment scenarios. Acceptance rule: A test is considered passed only when execution evidence demonstrates that its expected result occurred. ### 15. Implementation Roadmap & Operational Metrics - Start with artifact identity and training provenance. - Integrate evaluation and approval evidence. - Connect the registry to deployment records. - Add runtime verification and integrity controls. - Implement rollback tests and lineage gap monitoring. Core metrics: Verified production artifact coverage, metadata completeness, broken lineage references, digest mismatches, and time required to trace a production model to its training inputs. ### 16. FINAL MODEL LINEAGE DECISION - Recommended architecture: An artifact-centric provenance graph integrated with existing training, registry, storage, evaluation, and deployment systems. - Minimum required metadata: Immutable artifact ID, content digest, dataset snapshot, code revision, configuration, training run ID, evaluation evidence, approval record, deployment ID, and runtime artifact identity. - Critical relationships: Dataset → training run → artifact → evaluation → approval → deployment → actual runtime. - Highest-risk gaps: Unverifiable production artifact identity, missing training inputs, and approval evidence detached from the artifact. - Deployment verification strategy: Compare the expected approved artifact digest with the identity of the artifact actually loaded by the serving service. - Rollback requirement: Record the restored artifact, rollback action, and verified final runtime state. - Security controls: Least-privilege permissions, digest verification, auditable lineage changes, and explicit trust policies. - First implementation milestone: Generate immutable model artifact identities and capture their digests, then link them to training-run records. ## 🎯 MODEL LINEAGE READINESS VERDICT Insufficient Evidence Final verdict: Insufficient Evidence - The required lineage architecture has been designed for the hypothetical churn prediction system. - No actual datasets, training runs, registry records, evaluation reports, approval events, or production deployment evidence were supplied. - Therefore, existing traceability, reproducibility, integrity, and runtime verification cannot be confirmed. - This verdict does not mean the hypothetical system is defective; it means its current readiness cannot be established from the available evidence. Final recommendation: Implement artifact identity first, capture the full training-to-artifact chain next, and make verified runtime identity a mandatory production deployment control. Then execute the proposed validation suite before declaring the lineage architecture production-ready.