Catalog
method#Artificial Intelligence#Machine Learning#Software Engineering

AI Unified Process (AIUP)

A methodical lifecycle for developing, deploying and governing AI/ML systems. Combines iterative model work, MLOps automation and organizational controls.

The AI Unified Process (AIUP) is a pragmatic lifecycle model for developing, deploying, and governing AI/ML systems.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

Version control (Git) and CI/CD toolsFeature stores and data catalogsMonitoring and observability platforms

Principles & goals

Iterative development with short validation cyclesAutomation of pipelines to ensure reproducibilityIntegrated governance checks before production deployment
Iterate
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Overhead from excessive standardization
  • Incorrect governance may delay deployments
  • Insufficient monitoring can miss drift
  • Version data, models and pipelines consistently
  • Automate recurring checks and tests
  • Integrate governance checks early into the workflow

I/O & resources

  • Business goal and success criteria
  • Datasets with metadata and schemas
  • Infrastructure for CI/CD and monitoring
  • Versioned models and artifacts
  • Automated deployments and rollback plans
  • Governance reports and audit logs

Description

The AI Unified Process (AIUP) is a pragmatic lifecycle model for developing, deploying, and governing AI/ML systems. It integrates iterative model development, MLOps automation, and organizational controls into a single workflow. AIUP promotes governance, reproducibility, and continuous improvement with adaptations for scale and risk profiles.

✔

  • Consistent practices across teams to reduce technical risk
  • Improved reproducibility and auditability of models
  • Faster iterations via automated MLOps pipelines

✖

  • Increased onboarding effort for small teams
  • Requires disciplined data collection and versioning
  • Not all projects justify full process adoption

  • Time-to-Production

    Time from prototype to stable production release.

  • Model Drift Rate

    Frequency and magnitude of performance shifts in production.

  • Reproducibility index

    Share of results that can be reproduced using stored artifacts.

Retail personalization

A retailer used AIUP to standardize feature engineering, testing and monitoring before live rollout.

Predictive maintenance

A manufacturer implemented AIUP to integrate sensor data, validation and product risk analysis.

Credit risk scoring

A financial services provider used AIUP to ensure compliance checks, explainability, and auditability.

1

Assess current practice and toolchain

2

Define process building blocks and governance checks

3

Automate training, test and deploy pipelines

4

Train teams and roll out incrementally

⚠️ Technical debt & bottlenecks

  • Manual steps in the training pipeline
  • Unversioned configurations and hyperparameters
  • Missing standardized monitoring interfaces
Data quality and accessScalable infrastructure for retrainingSkills in monitoring and observability
  • Applying full methodology for a one-off experiment
  • Governance blockages preventing rapid validation
  • Focusing only on technical aspects without business metrics
  • Underestimating data preparation costs
  • Missing alerts for drift and performance loss
  • Too tight coupling of model and inference infrastructure
Knowledge in machine learning model developmentExperience with CI/CD, containerization and MLOpsCompetence in data quality, governance and compliance
Reproducibility of models and data pipelinesAutomated end-to-end pipelines (MLOps)Governance, compliance and explainability
  • • Regulatory requirements for sensitive data
  • • Limited resources for infrastructure automation
  • • Heterogeneous tool landscape in the organization