Golden paths are the CoE’s secret weapon for accelerating AI delivery. They are pre-approved, well-documented, and battle-tested patterns that teams can adopt without needing to reinvent the wheel or navigate lengthy approval cycles.
Think of them as “the easy way that’s also the right way.”
What Are Golden Paths?
Golden paths are:
- Reference architectures — Blueprints for common AI patterns (batch inference, real-time scoring, LLM applications)
- Approved tools and platforms — The technology stack that has passed security, legal, and cost reviews
- Templates and checklists — Standardised approaches for data pipelines, model training, deployment, monitoring
- Non-negotiables — The controls that every solution must implement (identity, logging, secrets management, data contracts)
Why they matter:
- Speed: Teams don’t waste time debating architecture or negotiating approvals
- Quality: Patterns are proven, tested, and optimised
- Consistency: Reduces technical debt and makes solutions supportable
- Risk reduction: Security, privacy, and compliance are baked in
Core Golden Paths
1. Data Ingestion and Feature Engineering
Use case: Get data from source systems into a state suitable for model training or inference.
Pattern:
Source System → Ingestion Pipeline → Data Lake/Warehouse → Feature Store → Model Training/Inference
Components:
- Ingestion tools: Apache Kafka, AWS Kinesis, Azure Event Hubs, Fivetran
- Transformation: dbt, Apache Spark, Databricks, AWS Glue
- Feature store: Feast, Tecton, AWS SageMaker Feature Store
- Data contracts: Schema validation, SLA definitions, lineage tracking
Non-negotiables:
- Data must be catalogued (metadata, lineage, ownership)
- Personal data must have lawful basis and retention policy
- Data quality checks before training/inference
- Access controls (least privilege, role-based)
Template: Data pipeline blueprint with CI/CD integration
2. Model Training and Evaluation
Use case: Train, tune, and evaluate models with reproducibility and auditability.
Pattern:
Feature Data → Experiment Tracking → Model Training → Hyperparameter Tuning → Evaluation → Model Registry
Components:
- Experiment tracking: MLflow, Weights & Biases, Neptune.ai
- Training infrastructure: Kubernetes, AWS SageMaker, Azure ML, Databricks
- Hyperparameter tuning: Optuna, Hyperopt, Ray Tune
- Evaluation framework: Custom harnesses, EvidentlyAI, Fiddler
- Model registry: MLflow Registry, AWS SageMaker, Azure ML
Non-negotiables:
- All experiments logged with parameters, metrics, and artefacts
- Models pass bias, accuracy, and robustness thresholds before promotion
- Evaluation results stored for audit trail
- Model versioning and lineage tracked
Template: Training pipeline with CI/CD and automated evaluation gates
3. Model Deployment and Serving
Use case: Deploy models to production with rollback capability, monitoring, and SLA adherence.
Pattern:
Model Registry → CI/CD Pipeline → Staging → Pre-Prod Testing → Production → Monitoring
Components:
- Deployment tools: Kubernetes, AWS SageMaker Endpoints, Azure ML Online Endpoints, Seldon Core
- CI/CD: GitHub Actions, GitLab CI, Azure DevOps, Jenkins
- API Gateway: Kong, AWS API Gateway, Azure API Management
- Rollback automation: Blue/green deployments, canary releases
Non-negotiables:
- All deployments via CI/CD (no manual deployments)
- Rollback plan tested and documented
- Health checks and readiness probes configured
- SLAs defined (latency, throughput, availability)
Template: Deployment pipeline with staging gates and rollback automation
4. Monitoring and Observability
Use case: Track model performance, data quality, drift, and incidents in production.
Pattern:
Production Model → Logging → Metrics Collection → Drift Detection → Alerting → Incident Response
Components:
- Logging: AWS CloudWatch, Azure Monitor, Datadog, Splunk
- Metrics: Prometheus, Grafana, New Relic
- Drift detection: EvidentlyAI, Fiddler, Arize, custom scripts
- Alerting: PagerDuty, Opsgenie, Slack
Non-negotiables:
- Model predictions logged (with consent and data minimisation)
- Drift monitoring for input data and model outputs
- SLO/SLA dashboards visible to stakeholders
- Runbooks for common incidents
Template: Observability stack with pre-configured dashboards and alerts
5. Prompt Design and LLM Applications
Use case: Build safe, reliable applications powered by Large Language Models.
Pattern:
Prompt Design → Evaluation Harness → Guardrails → Human-in-the-Loop → Logging → Monitoring
Components:
- Prompt engineering: Structured templates, few-shot examples, chain-of-thought
- Evaluation: ROUGE, BLEU, custom eval functions, human raters
- Guardrails: Content filters (Azure Content Safety, OpenAI Moderation API), PII redaction
- Human review: Flag uncertain responses for human validation
- Logging: Capture prompts, responses, and user feedback
Non-negotiables:
- Content moderation before displaying responses
- PII detection and redaction
- Human review for high-risk decisions
- Prompt injection defences (input sanitisation, system prompts)
- Rate limiting and cost controls
Template: LLM application blueprint with evaluation and guardrails
6. Risk and Compliance Reviews
Use case: Ensure AI solutions meet ethical, security, privacy, and regulatory standards.
Pattern:
Design → DPIA (privacy) → Threat Model (security) → Bias/Harm Test (ethics) → Compliance Review → Approval/Conditions/Decline
Components:
- Privacy: Data Protection Impact Assessment (DPIA) template
- Security: Threat modelling framework (STRIDE, PASTA)
- Ethics: Bias testing (Fairlearn, AI Fairness 360), red-teaming playbook
- Compliance: Audit trail, decision log, transparency documentation
Non-negotiables:
- High-risk use cases require full reviews before launch
- Documentation of risk mitigations and residual risks
- Approval signatures from Risk, Legal, and Compliance leads
Template: Risk review checklist and approval workflow
Approved Technology Stack
The CoE maintains a list of approved tools that have passed security, legal, and cost reviews. Teams are strongly encouraged to use these; alternatives require Architecture Review Board approval.
Example Approved Stack
| Layer | Tools |
|---|---|
| Data Ingestion | Kafka, Fivetran, AWS Glue |
| Data Storage | Snowflake, Databricks, AWS S3, Azure Data Lake |
| Feature Store | Feast, Tecton, SageMaker Feature Store |
| Experiment Tracking | MLflow, Weights & Biases |
| Training | SageMaker, Azure ML, Databricks, Kubernetes |
| Model Registry | MLflow, SageMaker, Azure ML |
| Deployment | SageMaker Endpoints, Azure ML Endpoints, Kubernetes |
| Monitoring | CloudWatch, Azure Monitor, Datadog, EvidentlyAI |
| LLM APIs | OpenAI, Azure OpenAI Service, Anthropic Claude |
| Guardrails | Azure Content Safety, OpenAI Moderation, LangKit |
| Logging | Splunk, CloudWatch Logs, Azure Log Analytics |
Non-Negotiables (The Hard Rules)
Every AI solution must comply with these architectural principles:
Identity and Access
- Principle: Least privilege; role-based access control (RBAC)
- Implementation: Azure AD, AWS IAM, Okta, SSO integration
Secrets Management
- Principle: No hardcoded secrets; rotate credentials regularly
- Implementation: AWS Secrets Manager, Azure Key Vault, HashiCorp Vault
Logging and Observability
- Principle: All significant events logged; audit trail maintained
- Implementation: Structured logging, retention policies, tamper-proof logs
Data Contracts
- Principle: Clear ownership, SLAs, and schema versioning
- Implementation: Data catalog, lineage tracking, quality checks
Privacy by Design
- Principle: Data minimisation, purpose limitation, retention limits
- Implementation: DPIA, anonymisation, consent management
Model Lifecycle
- Principle: Versioning, registry, evaluation, rollback capability
- Implementation: MLflow, SageMaker Registry, Azure ML Model Management
Golden Path Adoption Metrics
To track whether golden paths are genuinely helpful:
- Adoption rate: % of new AI initiatives using golden paths
- Time-to-value: Days from idea to production (faster with golden paths)
- Architecture review cycle time: Reduced when golden paths are used
- Incident frequency: Lower for solutions on golden paths
- Reuse: Number of components/patterns reused across initiatives
Target: >80% of new initiatives adopt golden paths within 6 months
Evolving Golden Paths
Golden paths are not static. The CoE should:
- Solicit feedback from delivery teams via Community of Practice
- Incorporate lessons learned from post-mortems and retrospectives
- Update patterns as new tools, threats, or regulations emerge
- Deprecate obsolete patterns and provide migration paths
Cadence: Quarterly review and update cycle
Next Steps
With golden paths defined, the next step is setting up the 90-day launch plan to stand up your CoE and start delivering value.