FIG. MLOP

SERVICES

MLOps Consulting

Stage: Services

Standardize ML/LLM lifecycle—data, models, deployment, and monitoring—with governance that stands up to audit.

10 min readLast updated: Q3 2026

Key Takeaways

ML / AI Platforms
Lifecycle Architecture & Governance
Assess & Design Governance

§01Technology Stack

ML / AI Platforms

MLflow Kubeflow Vertex AI SageMaker Databricks ML

Orchestration / Workflow

Airflow Argo Workflows

Model Serving / Deployment

KServe Seldon BentoML

Feature Store

Feast

Data & Model Versioning

DVC

Observability / Monitoring

Evidently Arize WhyLabs Prometheus Grafana

Containerization / Deployment

Docker Kubernetes

Data Lineage / Governance

OpenLineage

§02From Notebook to Production (Safely)

Lifecycle Architecture & Governance

Tooling rail: MLflow/Kubeflow • registries • approvals Edge: Promotion gates + audit trails via EthicΞense™.

Data/Feature Management & Reproducibility

Tooling rail: Feast • DVC • lineage • tests Edge: Contract-first features with versioned provenance.

CI/CD & Automated Training Pipelines

Tooling rail: Argo/Airflow • cached steps • artifacts Edge: Deterministic builds; environment parity by design.

Serving & Deployment (Batch/Real-time/Streaming)

Tooling rail: KServe/Seldon/BentoML • canary • A/B Edge: Qμβrix™ SLOs for latency, error, and throughput.

Monitoring, Drift & Model Risk

Tooling rail: Evidently • Arize/WhyLabs • PSI/KS • alerts Edge: Risk tiers, playbooks, and rollback triggers pre-agreed.

LLMOps & Inference FinOps

Tooling rail: routers • caching • quantization • vLLM Edge: Neuro-Quantis™ enforces budgets; ReinΩlytix™ ties value to cost.

§03Methodology

Assess & Design Governance

Lifecycle + risk tiers + KPIs.

Automate & Secure CI/CD

Pipelines, registry, gates.

Operate & Improve Ops

SLOs, drift response, FinOps.

§04Production ML you can trust

Registry-based promotions with approvals
Feature stores and reproducible data lineage
Canary/A-B serving with rollback
Drift detection and incident playbooks
LLMOps and inference cost controls

§05Assurance Strip

Assurance Strip

Registry: Centralized repository for managing, versioning, and tracking machine learning models and artifacts
Canary: Gradual deployment strategy to test new models or features in production while minimizing risk
Drift: Monitoring and managing changes in data or model performance to maintain accuracy and reliability
Budgets: Tracking and controlling expenditures across AI projects, including development, deployment, and operations

Boardroom Kit

MLOps Blueprint: Comprehensive framework outlining processes, tools, and standards for managing the ML lifecycle efficiently
Promotion Gate Policy: Guidelines and checkpoints for promoting models from development to production while ensuring quality and compliance
Drift & Incident Playbook: Standardized procedures to detect, respond to, and mitigate model drift and operational incidents in production
Serving SLO Pack: Collection of service-level objectives defining performance, availability, and reliability standards for model serving

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