4.2 Development & Validation

The Development & Validation phase is where AI products take shape. Unlike traditional software where requirements translate predictably to code, AI development is inherently experimental—models may not converge, data may reveal unexpected patterns, and "good enough" performance requires judgment. The AI Innovation approach embraces this uncertainty while maintaining disciplined governance throughout.

The Development Philosophy

AI development is not a waterfall process. It's a series of experiments, each informing the next. The pod's job is to fail fast and learn faster, iterating toward a model that meets the success criteria defined in the Model Card while maintaining the guardrails established during chartering.

Data Phase

Data Acquisition & Curation

Most AI projects fail because of data problems, not model problems. The data phase deserves significant attention:

1

Data Inventory

Document all data sources identified during chartering. For each source, capture: owner, access method, freshness, quality assessment, consent status, and any restrictions.

2

Data Access & Pipelines

Build reliable, auditable pipelines to access required data. Implement proper authentication, logging, and error handling. Document data lineage from source to training set.

3

Data Quality Assessment

Profile data for completeness, accuracy, consistency, and timeliness. Identify and document quality issues. Determine whether issues are fixable or require data source changes.

4

Bias & Representation Analysis

Analyze data for representation across protected groups. Identify underrepresented populations. Document gaps and their potential impact on model fairness.

Data Governance Checkpoints

The Ethics Liaison validates data practices at key points:

Checkpoint Validation Documentation
Source Approval Legal basis for using each data source Data consent records, licenses
Privacy Review PII handling, anonymization effectiveness Privacy impact assessment
Representation Review Adequate coverage of relevant populations Demographic analysis
Labeling Quality Annotation process fairness and accuracy Labeling guidelines, quality metrics

Model Development

Iterative Development Cycle

Model development follows an iterative pattern within the Agile for AI framework:

Cycle Start

Hypothesis Formation

Define what you're trying to achieve this iteration. What approach will you try? What would success look like? What would make you abandon this direction?

Experiment

Rapid Prototyping

Implement the minimum viable version of the approach. Use notebooks, simplified data, and quick iterations. Don't build production infrastructure for experiments.

Evaluate

Results Analysis

Measure against success criteria. Compare to baselines. Analyze failure modes. Document learnings regardless of outcome.

Decide

Continue, Pivot, or Stop

Based on results, choose next direction. Promising results → refine approach. Poor results → try different approach. Repeated failure → reconsider feasibility.

Development Best Practices

Version Everything

Code, data, models, and experiments should all be versioned and reproducible. Use MLflow, DVC, or similar tools.

Automate Early

Training pipelines should be automated from the start. Manual processes don't scale and create reproducibility debt.

Test Continuously

Unit tests for data pipelines, integration tests for model serving, and model tests for performance—all in CI/CD.

Document As You Go

Update the Model Card with each significant decision. Capture the "why" not just the "what."

Validation & Testing

Multi-Layer Testing Strategy

AI products require testing at multiple levels:

Test Layer Purpose Examples
Unit Tests Individual component correctness Data transformations, feature engineering, utility functions
Model Tests Model performance against specifications Accuracy thresholds, latency requirements, resource usage
Fairness Tests Equitable performance across groups Demographic parity, equalized odds, calibration
Integration Tests End-to-end system behavior API contracts, downstream system interactions
Adversarial Tests Robustness to malicious inputs Prompt injection, data poisoning, edge cases
User Acceptance Real-world usability and value Domain expert review, pilot user feedback

Fairness Testing Framework

Before any deployment, validate fairness across identified protected groups:

Fairness Validation Checklist
  • Define protected attributes and relevant subgroups
  • Select appropriate fairness metrics for the use case
  • Establish acceptable disparity thresholds
  • Measure performance disaggregated by subgroup
  • Compare subgroup metrics against thresholds
  • Investigate and document any disparities found
  • Implement mitigations for unacceptable disparities
  • Re-test after mitigation to validate improvement

Validation Sign-Off

Before proceeding to deployment, formal validation sign-off is required:

Sign-Off Who What They're Certifying
Technical ML Engineer Lead Model meets performance requirements and is production-ready
Quality QA / Testing All required tests pass and edge cases are addressed
Ethics AI Ethics Liaison Fairness tests pass and governance requirements are met
Product STO Model delivers expected business value and is ready for users

Deployment Preparation

Production Readiness Checklist

Before deployment, validate operational readiness:

Infrastructure

  • Compute resources provisioned
  • Model artifacts stored securely
  • Serving infrastructure configured
  • Scaling policies defined

Monitoring

  • Performance metrics instrumented
  • Alerting thresholds configured
  • Dashboards created
  • On-call rotation established

Documentation

  • Model Card updated with final metrics
  • Runbooks for common issues
  • API documentation complete
  • User guides prepared

Rollback

  • Previous version preserved
  • Rollback procedure documented
  • Rollback tested
  • Kill switch available

Deployment Plan

Document the deployment approach before execution: