AI Pipeline and MLOps Maturity Assessment
A 65-question diagnostic across 9 domains that evaluates the engineering system that builds, releases and runs ML and GenAI models: platform, ML security, data and labels, evaluation and validation, LLMOps, release, obse...
Overview
A 65-question diagnostic across 9 domains that evaluates the engineering system that builds, releases and runs ML and GenAI models: platform, ML security, data and labels, evaluation and validation, LLMOps, release, observability, serving and improvement. Written for the head of ML platform or AI engineering who needs to answer: can we ship and run models and GenAI applications reliably, reproducibly and safely, or does each one depend on the people who built it?
Detailed Description
Many ML and GenAI programs stall between a promising notebook or prompt and a dependable production service. Models are trained by hand, prompts change without testing, retrieval indexes go stale and drift is found by customers.
This assessment follows the order in which a dependable ML capability is built: a lifecycle standard scaled to risk, a platform run as a product, and ML-specific security; then automated dataset, lineage, quality, feature and label practice; tracked experiments, a model registry, evaluation, peer review and independent validation; prompt versioning, evaluation harnesses, retrieval, guardrails, a model gateway and agent runtime controls; automated pipelines with adversarial tests, risk-tiered gates, human oversight, rollout and rollback; logging, drift, skew, tracing and retraining; serving objectives, fallback, incidents, unit cost and retirement; and delivery measures and improvement.
It tests whether practice is enforced by the pipeline, not only written down. Questions marked (Bridge) check the link to the model that owns a neighboring practice: AI Governance, DevOps Engineering, DevSecOps, Non-Human Identity and Secrets Management, SRE, DevOps and ITSM Convergence, AI Spend Management, Capacity Performance Management, AI Use Case Lifecycle Management, Post-Incident Review and Continual Service Improvement. Each question is scored on whether the practice is documented, implemented and effective.
Assessment Details
Audience
Head of ML platform, head of AI engineering, MLOps director. Secondary: data science and data engineering leads, model risk and validation teams, AI security engineers and SRE leads.
Purpose
Shows where ML and GenAI delivery still depends on manual steps and individuals, where models cannot be reproduced, validated or rolled back, where GenAI changes ship untested, and where failures and drift go unseen. Gives each gap a concrete next step and a measurable target.
Effort
Initial assessment: 5 to 7 hours of facilitated sessions with ML platform, data science, data engineering, model risk, AI security and operations. Evidence gathering: pipeline definitions and run history, registry and tracking records, evaluation and validation reports, gateway and guardrail configuration, monitoring and incident records.
Cadence
Every six months, and after a major platform change, a serious model incident or a change in AI regulation that affects delivery.
Framework Alignment
KPIs
- Registered Production Models - Share of production models served from a registry version. Target: 100%.
- Harness-Gated GenAI Changes - Share of production GenAI changes released only after passing the evaluation harness. Target: 100%.
- Validated High-Risk Models - Share of high-risk models in use with a current independent validation. Target: 100%.
- Drift Alerts Closed in Time - Share of drift alerts classified and closed within the agreed time. Target: 90% or higher.
- Model Lead Time - Median days from the first committed model change to production. Target: 7 days or fewer (DORA 2024 high band, applied to model changes).
Dependencies
Assessment Outline
(9 domains · 65 questions)-
1
1 MLOps Operating Model and Platform Foundations
Lifecycle standard scaled to risk tiers, platform reference architecture, platform as a product, environments, infrastructure as code, endpoint reconciliation with the AI System Inventory and accelerator quotas
- 1.1 MLOps Lifecycle Standard 1 questions
- 1.2 ML Platform Reference Architecture 1 questions
- 1.3 ML Platform as a Product 1 questions
- 1.4 Environment Separation and Parity 1 questions
- 1.5 ML Infrastructure as Code (Bridge) 1 questions
- 1.6 Endpoint Reconciliation with the AI System Inventory (Bridge) 1 questions
- 1.7 Accelerator Quotas and Scheduling (Bridge) 1 questions
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2
2 ML Security Engineering
Access to ML assets, applied threat models, ML images and libraries, secrets in notebooks and pipelines, provenance of external models and safe model file formats
- 2.1 Access Control for ML Assets 1 questions
- 2.2 Applied ML Threat Modeling 1 questions
- 2.3 ML Libraries and Images (Bridge) 1 questions
- 2.4 Secrets in ML Workflows (Bridge) 1 questions
- 2.5 External Model and Dataset Provenance 1 questions
- 2.6 Model Artifact Integrity and Safe Formats 1 questions
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3
3 Data, Feature and Label Engineering
Automated dataset and lineage capture, sensitive data handling in pipelines, dataset versions, data quality gates, feature management, feed freshness and labeling
- 3.1 Automated Dataset Capture (Bridge) 1 questions
- 3.2 Automated Lineage Capture (Bridge) 1 questions
- 3.3 Sensitive Data in ML Pipelines (Bridge) 1 questions
- 3.4 Dataset Versioning 1 questions
- 3.5 Automated Data Quality Gates 1 questions
- 3.6 Feature Management 1 questions
- 3.7 Data Feed Freshness 1 questions
- 3.8 Labeling Process and Quality 1 questions
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4
4 Model Development, Evaluation and Validation
Experiment tracking, the model registry, reproducibility, evaluation with baseline comparison, responsible AI tests in the pipeline, peer review, independent validation and system card data
- 4.1 Experiment Tracking 1 questions
- 4.2 Model Registry 1 questions
- 4.3 Model Reproducibility 1 questions
- 4.4 Model Evaluation and Baseline Comparison 1 questions
- 4.5 Responsible AI Tests in the Pipeline (Bridge) 1 questions
- 4.6 Peer Review of Model Work 1 questions
- 4.7 Independent Model Validation 1 questions
- 4.8 System Card Data from Records (Bridge) 1 questions
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5
5 LLMOps and GenAI Engineering
Prompt and configuration versioning, evaluation harness and golden sets, RAG ingestion and indexes, permission-aware retrieval, fine-tuning controls, guardrails, model gateway, provider model changes and agent deployment
- 5.1 Prompt and Configuration Versioning 1 questions
- 5.2 GenAI Evaluation Harness and Golden Sets 1 questions
- 5.3 RAG Ingestion and Index Management 1 questions
- 5.4 Permission-Aware Retrieval 1 questions
- 5.5 Fine-Tuning and Adaptation Controls 1 questions
- 5.6 Runtime Guardrails 1 questions
- 5.7 Model Gateway and Routing (Bridge) 1 questions
- 5.8 Provider Model Version Changes 1 questions
- 5.9 Agent Deployment Engineering (Bridge) 1 questions
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6
6 Pipeline Automation and Secure Release
Automated training pipelines, ML checks in the delivery pipeline, automated adversarial testing, risk-tiered promotion gates, human oversight built in, progressive rollout and rollback
- 6.1 Automated Training Pipeline 1 questions
- 6.2 ML Checks in the Delivery Pipeline (Bridge) 1 questions
- 6.3 Automated Adversarial Testing 1 questions
- 6.4 Risk-Tiered Promotion Gates 1 questions
- 6.5 Human Oversight Built into Release 1 questions
- 6.6 Progressive Rollout 1 questions
- 6.7 Model Rollback 1 questions
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7
7 Observability, Drift and Feedback
Inference logging, quality monitoring, drift detection, training-serving skew, end-to-end tracing, GenAI tracing and sampling, alert routing, retraining and feedback
- 7.1 Inference Logging (Bridge) 1 questions
- 7.2 Prediction Quality Monitoring 1 questions
- 7.3 Data and Concept Drift Detection 1 questions
- 7.4 Training-Serving Skew 1 questions
- 7.5 End-to-End Inference Tracing 1 questions
- 7.6 GenAI Tracing and Response Sampling 1 questions
- 7.7 Alert Routing and Tuning 1 questions
- 7.8 Retraining and Feedback Loop 1 questions
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8
8 Serving Reliability, Resilience and Cost
Serving objectives, scaling, fallback, ML incident response, unit cost, optimization and model retirement
- 8.1 Serving Objectives (Bridge) 1 questions
- 8.2 Serving Scalability 1 questions
- 8.3 Fallback and Safe Defaults 1 questions
- 8.4 ML Incident Response (Bridge) 1 questions
- 8.5 Unit Cost of Inference (Bridge) 1 questions
- 8.6 Inference Optimization 1 questions
- 8.7 Model Retirement (Bridge) 1 questions
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9
9 MLOps Measurement and Improvement
ML delivery measures, ML technical debt, platform adoption and satisfaction, learning from ML incidents and the improvement roadmap
- 9.1 ML Delivery Measures 1 questions
- 9.2 ML Technical Debt 1 questions
- 9.3 Platform Satisfaction and Feedback 1 questions
- 9.4 Learning from ML Incidents (Bridge) 1 questions
- 9.5 MLOps Improvement Roadmap (Bridge) 1 questions
At a Glance
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