Mind Matrix: Navigation
SOP-1020-01-AI_AI-Model-Lifecycle-Management
Title: AI Model Lifecycle Management

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Effective Date: YYYY-MM-DD
Previous Version: None
Reason for Update: New SOP
Owner: Chief AI Officer (CAIO)
Location: [Designated Document Repository]
Signature/Date:

[Signature block for Owner]

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Objective

The objective of this Standard Operating Procedure (SOP) is to define a structured approach for the entire lifecycle of AI models, from initial concept and data sourcing through development, validation, deployment, monitoring, retraining, and eventual decommissioning or retirement. This ensures consistent, high-quality processes, while adhering to ethical, regulatory, and operational standards (including AI-IRB oversight).

Scope

This SOP applies to all AI models developed, maintained, or deployed under the organization’s purview. It encompasses:

• Model Ideation and Design (business objectives, feasibility),
• Dataset Acquisition and Preprocessing (data ingestion, cleaning, labeling),
• Model Development (selection of architectures, hyperparameter tuning),
• Validation/Testing (model evaluation, interpretability checks),
• AI-IRB or external regulatory approvals,
• Deployment (production environment, CI/CD),
• Monitoring (performance, drift detection, compliance),
• Maintenance (bug fixes, enhancements),
• Retirement (archival or full removal).

All teams and roles involved in the creation, oversight, or usage of AI-based technologies must comply with this SOP.

Definitions

• AI-IRB: The Artificial Intelligence Institutional Review Board or function that oversees ethical/regulatory aspects of AI systems.
• Data Drift: Changes in the statistical properties of data that cause model performance degradation.
• Model Drift: Situations where model predictions degrade over time due to changes in the environment or data patterns.
• MLOps: End-to-end process bridging data science and operations to deploy and maintain machine learning models efficiently and reliably.
• Authorized AI Agent: A validated AI system or subsystem identified within the Mind Matrix as having the authority to execute specific SDLC or operational tasks. |

Roles and Responsibilities

Role	Responsibility
Project Sponsor	Allocates budget, approves major AI-IRB initiatives, resolves high-level scope changes.
AI-IRB Liaison	Coordinates AI-IRB reviews and approvals, ensures compliance with ethical/regulatory guidelines.
Data Scientist	Designs models, orchestrates dataset collection, runs experiments. Documents approach for AI-IRB compliance.
MLOps/Operations	Manages environment setups for training, staging, and production. Oversees CI/CD pipelines, monitors model behavior.
Quality Assurance	Reviews model output quality, ensures SOP adherence, verifies compliance with AI-IRB conditions.
Development Team	Implements application logic, integrates the model into the software solution, handles version control.
Business Stakeholder	Outlines functional requirements and success criteria, provides domain knowledge, and ensures business alignment.
Model Owner	Accountable for the model’s business performance, organizes periodic reviews with QA and AI-IRB Liaison, can request retraining or retirement.

Metrics

1. Time from Development to Deployment
   Measures the cycle time for an AI model to move from proof-of-concept to production.
2. Number of Model Retrains
   Indicates how often the model is updated or retrained due to new data or performance concerns.
3. Model Accuracy/Performance
   Tracks changes in performance metrics (e.g., F1 score, ROC-AUC, MSE) during the lifecycle.
4. Drift Incidents
   The number of times data or model drift triggers re-evaluation or retraining.
5. Compliance/IRB Incidents
   Count of compliance-related issues flagged by AI-IRB or other regulators.

Procedure Activities

Below are the major phases and activities in the AI Model Lifecycle Management process:

1. Model Ideation & Requirements Gathering
   • Business Stakeholder documents project objectives, success metrics, and constraints.
   • Data Scientist outlines high-level feasibility.
   • AI-IRB Liaison performs a preliminary ethics review.
2. Data Collection & Preparation
   • Data Scientist identifies data sources, performs data profiling, cleaning, labeling.
   • If personal or sensitive data is used, AI-IRB Liaison verifies compliance with relevant privacy regulations.
3. Model Development
   • Data Scientist trains model candidates, tunes hyperparameters, logs experiments.
   • Development Team manages code structure, version control.
   • Quality Assurance reviews partial results for correctness, potential biases.
4. Internal Validation & Testing
   • Data Scientist performs cross-validation, calculates relevant metrics (precision, recall, etc.).
   • Quality Assurance checks model interpretability documentation, addresses bias or drift potential.
5. AI-IRB & External Approval (If necessary)
   • AI-IRB Liaison compiles relevant compliance documents.
   • AI-IRB reviews usage, fairness, bias analysis, ethical constraints.
   • If not approved, proceed with necessary modifications.
6. Pre-Deployment Preparation
   • MLOps/Operations sets up the staging environment, configures CI/CD pipeline.
   • Development Team merges final code, confirms naming conventions, and environment variables.
   • Quality Assurance runs final acceptance tests in staging.
7. Deployment to Production
   • MLOps/Operations executes deployment plan, monitors logs for anomalies.
   • Model Owner or Business Stakeholder signs off on going live.
8. Post-Deployment Monitoring
   • MLOps/Operations monitors model performance, collects drift indicators, and audits recursive self-improvement subroutines for unintended behavioral shifts.
   • Data Scientist reviews metrics periodically, notifies Model Owner if performance degrades.
   • Quality Assurance ensures ongoing compliance with AI-IRB conditions.
9. Periodic Retraining & Maintenance
   • If drift or performance declines are detected, a retraining process is triggered, integrating Exochain Peer Reviews for automated re-validation and consistency checks.
   • Data Scientist re-trains or updates feature engineering.
   • Development Team merges new model version, ensures backward compatibility if required.
10. Model Retirement

• Model Owner initiates retirement if model no longer meets business or compliance needs.
• MLOps/Operations archives relevant model artifacts and data.
• AI-IRB Liaison verifies documentation for final compliance records.

Forms

• No dedicated forms in this SOP.
  However, relevant existing forms for IRB compliance and data usage logs should be used (e.g., AI-IRB Application Forms, Data Access Logs).

Exemptions

• Proof-of-concept prototypes not used in production are exempt from some steps, but still must comply with relevant AI-IRB requirements if real data is used.
• If an existing enterprise policy supersedes a section of this SOP, that enterprise policy takes precedence, but must be documented as a recognized exemption.

Tools/Software/Technology Used

Tool	Purpose
Git/Version Control	Manages code and model versioning.
MLFlow or DVC	Logs model training parameters, metrics, and artifacts.
Docker/Kubernetes	Containerizes ML code for consistent deployment.
Cloud Monitoring (e.g., AWS/GCP)	Observes model performance, triggers alerts.
AI-IRB Compliance Portal	Submits and tracks IRB approvals.

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Revision History

Version	Date	Description	Author/Editor
1.0	YYYY-MM-DD	Initial release of SOP 1020-01-AI	Name/Signature

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Note: All participants in the AI model development lifecycle must read and understand this SOP before engaging in new projects or making modifications to existing models. Non-compliance may result in project delays, quality concerns, or regulatory violations.

@startuml
title SOP-1020-01-AI: AI Model Lifecycle Management

’ Define participants with short names
participant “Project Sponsor” as PS
participant “AI-IRB Liaison” as AIRB
participant “Data Scientist” as DS
participant “MLOps/Operations” as MLOps
participant “Quality Assurance” as QA
participant “Development Team” as Dev
participant “Business Stakeholder” as BS
participant “Model Owner” as MO

’ 1) Model Ideation & Requirements Gathering
BS -> DS: Provide Business Objectives & Constraints
DS -> PS: Present High-Level Feasibility
PS -> AIRB: Request Preliminary AI-IRB Ethical Check
AIRB -> DS: Provide Early Ethics Feedback

’ 2) Data Collection & Preparation
DS -> DS: Ingest & Clean Data
DS -> DS: Label & Validate Data
DS -> AIRB: Confirm Data Compliance Requirements

’ 3) Model Development
DS -> Dev: Request Code Setup & Version Control
Dev -> DS: Provide Development Environment
DS -> DS: Train Model(s), Tune Hyperparams
DS -> QA: Share Preliminary Model Results

’ 4) Internal Validation & Testing
QA -> DS: Review Model Performance & Potential Bias
DS -> QA: Provide Model Interpretability Docs
QA -> DS: Request Additional Adjustments (if needed)

’ 5) AI-IRB & External Approval
DS -> AIRB: Submit Full Documentation & Compliance
alt AI-IRB Approved?
else Not Approved
AIRB -> DS: Provide Revisions Required
DS -> DS: Revise & Address Concerns
DS -> AIRB: Resubmit for Approval
end
AIRB -> PS: AI-IRB Officially Approved

’ 6) Pre-Deployment Preparation
MLOps -> DS: Prepare Staging Environment
Dev -> MLOps: Merge Final Code for Deployment
QA -> MLOps: Validate Acceptance Tests in Staging
QA -> MO: Confirm readiness for Production

’ 7) Deployment to Production
MO -> MLOps: Authorize Production Rollout
MLOps -> MLOps: Deploy Model & Infrastructure
MLOps -> Dev: Notify Deployment Completion
Dev -> BS: Production Release Announced

’ 8) Post-Deployment Monitoring
MLOps -> DS: Send Performance & Drift Metrics
DS -> MO: Evaluate Model Behavior
alt If Performance Degrades
DS -> DS: Trigger Retraining or Tuning
else If Performance Stable
DS -> DS: Continue Monitoring
end

’ 9) Periodic Retraining & Maintenance
DS -> MLOps: Schedule Automatic Retraining Jobs
Dev -> QA: Check Updated Model for Regressions
QA -> MO: Approve Updated Model for Re-Deployment

’ 10) Model Retirement
MO -> DS: Initiate Retirement if Model is Obsolete
MLOps -> Dev: Archive Model Artifacts
AIRB -> MO: Verify Final Compliance Records
MO -> All: Confirm Model Retired & Document Lessons

@enduml

Short textual explanation:
This diagram shows the end-to-end AI Model Lifecycle under SOP-1020-01-AI. It starts with the business stakeholder providing objectives, data scientist preparing data, then moves through model development and validation, AI-IRB approval, deployment, post-deployment monitoring, and ends with model retraining or retirement. Decision blocks are used for AI-IRB approval and performance checks to illustrate possible paths.