Registry · Evaluation · Deployment · Monitoring

An end-to-end operating framework for managing ML and LLM systems across their lifecycle. We establish the tooling, processes, and guardrails to register, evaluate, promote, and monitor models (and prompts/RAG chains) so you can ship value quickly—with safety, traceability, and cost control.

• Teams scaling from ad-hoc models to a governed portfolio (ML + LLM/RAG)
   
• Leaders who need repeatable release gates, clear ownership, and audit-ready evidence
   
• Orgs with multiple clouds/tools seeking one consistent way to ship and run AI
   

• Unified model inventory & registry (ML + LLMs + prompts + RAG pipelines)
   
• Promotion workflow with evaluation gates, approvals, and rollback plans
   
• Automated CI/CD for models & prompts (policy-as-code, secrets hygiene)
   
• Online/offline evaluation harness with business/KPI alignment
   
• Production monitoring for quality, drift, bias, safety, latency, and cost
   
• Runbooks & RACI so ownership is unambiguous from experiment to sunset
   

1. Model Portfolio & Registry Setup
    
   • Canonical metadata (datasets, features, prompts, retrieval scope, evals, owners)
      
   • Model/Prompt/Chain cards; versioning & lineage into data products and endpoints
      

1. Release Process & Gates
    
   • Staging → canary → production flow with go/no-go criteria
      
   • Safety & privacy checks (PII leakage tests, jailbreak/prompt-injection evals)
      
   • Approval matrices (risk tiers, approvers, evidence)
      

1. Evaluation Framework
    
   • Offline: reference datasets, golden prompts, rubric/LLM-as-judge where appropriate
      
   • Online: A/B and interleaving test plans, guardrail metrics, SLOs/SLA definitions
      
   • Score aggregation, dashboards, and sign-off templates
      

1. CI/CD & Environments
    
   • Build artifacts (containers, model bundles), dependency locks, reproducibility
      
   • Policy-as-code checks (ownership, metadata completeness, PII flags)
      
   • Secrets/KMS integration, feature store alignment, vector index workflows
      

1. Observability & Incident Management
    
   • Telemetry: quality, drift, bias, hallucination/leakage rate, latency, cost per call
      
   • Alerting thresholds, incident runbooks, rollback and feature flag patterns
      
   • Post-incident review template and learning capture
      

1. Operating Model & Training
    
   • RACI across data science, platform, security, and product
      
   • Model intake workflow; deprecation/sunsetting policy
      
   • Hands-on enablement for engineers, DS/DA, and product owners
      

1. Executive Pack
    
   • Portfolio health, risk posture, value tracking, and near-term roadmap
      

Week 1: Discover & Baseline
Inventory current models/LLMs/prompts, pipelines, and tools; map gaps vs. target operating model.

Week 2–3: Design & Prove
Design registry schema, promotion gates, eval harness; prototype CI checks and monitoring on 1–2 priority use cases.

Week 4–6: Implement & Embed
Stand up registry + cards, integrate CI/CD and policy checks, wire observability, define runbooks, and execute first controlled promotion.

Week 7: Readout & Scale Plan
Finalize artifacts, assign ownership, and deliver a 2–3 quarter scale roadmap.

(Can compress/expand based on scope and platform readiness.)

• Model types: supervised/unsupervised/time-series, reinforcement, LLMs (prompt, RAG, tools/agents), fine-tuned and retrieval-grounded
   
• Platforms: cloud ML services, on-prem GPU, model registries, feature stores, vector DBs, orchestration/schedulers, gateways/proxies
   
• Controls: privacy (PII masking/scope), security (IAM, KMS, network), responsible-AI (bias/toxicity), access & approvals
   
• Integrations: issue trackers, experiment trackers, data catalogs/lineage, BI/analytics for KPI tie-out
   

• 100% of prod models/LLMs registered with cards, lineage, owners, and eval links
   
• Time from “ready to promote” → production ↓ 50% with gates met
   
• Online business KPI lift detected within 2 weeks of launch (or auto-rollback)
   
• Drift detection coverage ≥ 95% for Tier-1 models; MTTR on incidents ↓ 40%
   
• Prompt/chain changes tracked with reproducible evals 100% of the time
   

• Read-only access to current pipelines, registries, feature/vector stores, and monitoring
   
• Stakeholder time across DS/ML, platform, product, security, and legal/privacy
   
• Representative use cases to pilot (one ML, one LLM/RAG if applicable)
   

• “Hero model” releases: replace tribal processes with gated, auditable promotion
   
• Shadow prompts & unmanaged RAG: register chains, scope retrieval, and test for leakage
   
• Stale models & drift: continuous evals with actioned alerts and rollback paths
   
• Tool sprawl: one operating model across heterogeneous platforms
   

• Hands-on buildout of the evaluation harness (synthetic + human-in-the-loop)
   
• Independent red-team exercises for LLM/RAG and guardrail tuning
   
• Cost observability & optimization for inference and retrieval layers
   
• Vendor selection/migration for registry, feature store, vector DB, gateway
   

We blend delivery pragmatism with strong governance: your teams keep shipping, leadership gets assurance, and customers get better outcomes faster.

Share your current stack (registry/feature store/vector DB, CI/CD, monitoring) and 1–2 candidate use cases. We’ll tailor the pilot and scale plan to your environment and goals.