MLOps Consulting

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

Request an MLOps Readiness Review
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iconTechnology Stack
  • MLflow
  • Kubeflow
  • Vertex AI
  • SageMaker
  • Databricks ML
  • Airflow
  • Argo Workflows
  • KServe
  • Seldon
  • BentoML
  • Feast
  • DVC
  • Evidently
  • Arize
  • WhyLabs
  • Prometheus
  • Grafana
  • Docker
  • Kubernetes
  • OpenLineage

From 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.

Our Clients

Clients that trusted Us

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Methodology

Assess & DesignGovernance

Lifecycle + risk tiers + KPIs.

Automate & Secure CI/CD

Pipelines, registry, gates.

Operate & Improve Ops

SLOs, drift response, FinOps.

Production ML you can trust

Clear gates, reliable serving, continuous assurance.

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  • 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

Assurance Strip

Foundational safeguards ensuring compliance, accountability, and operational integrity.

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

Executive tools to govern, track, and fund AI initiatives responsibly.

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

Get Started – AI Efficiency with
Proven Performance

Accelerate AI workloads with rigorously tested Quantum-Inspired Neuromorphic AI.

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Services
United States

Strategemist Corporation
16192 Coastal Highway Lewes, Delaware 19958

United Kingdom

Strategemist Limited
71-75 Shelton Street,Covent Garden, London, WC2H 9JQ

India

Strategemist Global Private Limited
Cyber Towers 1st Floor, Q3-A2, Hitech City Rd, Madhapur, Telangana 500081, India

KSA

Strategemist - EIITC
Building No. 44, Ibn Katheer St, King Abdul Aziz, Unit A11, Riyadh 13334, Saudi Arabia