CMARIX offers MLOps consulting services for engineering groups who are keen on implementing machine learning frameworks. We offer solutions such as implementing ML frameworks, building CI/CD pipelines, managing feature stores, monitoring, and retraining.
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CMARIX provides MLOps consultation services across the full range, including strategy, pipeline development, model deployment, model monitoring, governance, and managed services. Each project is designed to fit your infrastructure and the production problems you face.
A production ML platform architecture built for your team, your stack, and your scale.
We assess your current ML infrastructure maturity, identify the gaps blocking reliable model deployment, and design a target-state MLOps platform that fits your cloud environment, data warehouse, and engineering workflows. MLOps decisions belong inside a broader data strategy, and we engage our data strategy consulting services to ensure your ML platform aligns with your organization's data architecture before a single pipeline is built.
Stack: Platform Assessment | Architecture Blueprint | Tool Selection | Roadmap | Governance Framework
Build a scalable MLOps foundation designed for reliability, automation, and long-term growth.
Our team builds production-ready MLOps platforms, which allow the integration of the entire ML pipeline from development through to deployment and monitoring into a coherent engineering process. Each such platform is tailored to suit your particular infrastructure and objectives.
Stack: Kubernetes | Kubeflow | MLflow | Docker | Terraform | AWS SageMaker | Azure ML | Vertex AI | Databricks
Automated training, testing, and deployment pipelines that treat models like production software.
ML CI/CD pipelines automate training, model validation against predefined accuracy levels, artifact creation, and step-by-step deployments to production. They include regression tests that use a hold-out set to avoid deploying poor-quality models to production. MLOps is part of the target-state architecture in enterprise architecture consulting services.
Stack: Kubeflow Pipelines | MLflow | GitHub Actions | DVC | Weights & Biases | Docker | Kubernetes | ArgoCD
Consistent, reusable features across training and serving, without data skew.
We create and deploy feature stores that eliminate training-serving skew, enable reuse of features among model teams, and guarantee consistent transformation logic during both training and inference. For ML platform engineering at scale, a common feature store provides the infrastructure that enables model teams to work more quickly without data pipeline redundancy.
Stack: Feast | Tecton | Databricks Feature Store | Hopsworks | Redis | Kafka | dbt | Apache Spark
Know when a model's performance degrades before your users do.
Our production models are equipped with alerts for data drift, prediction drift, feature distributions, and correlations between features and KPIs. Each alert is defined with a threshold, severity, and an action - whether to automatically retrain the model, to require human approval to take action, or to revert to the previous version of the model. MLOps and DataOps are closely related, and we provide this service together with our data engineering services.
Stack: Evidently AI | Arize AI | WhyLabs | Grafana | Prometheus | Great Expectations | LangSmith
Detect model degradation early and automate retraining before it affects business performance.
Continuous monitoring is used to ensure model accuracy, data quality, and the absence of feature and prediction drift, with automated alerts and retraining pipelines. All monitoring pipelines have unique business-oriented thresholds to ensure model robustness and mitigate production risks.
Stack: Evidently AI | Arize AI | WhyLabs | Grafana | Prometheus | Great Expectations | Airflow | MLflow
Audit-ready ML operations with full lineage, versioning, and access controls.
Model registries, experiment tracking, dataset versioning, lineage graphs, and role-based access control ensure each model in production can be traced back to the data it was trained on, the engineer who approved it, and the evaluation metrics that justified its deployment. Model governance goes hand in hand with data governance, and we relate this to our data governance consulting offerings.
Stack: MLflow Model Registry | DVC | Delta Lake | Apache Atlas | OPA | Audit Logging | RBAC
Production ML operations are run by CMARIX, so your data science team can focus on modeling.
We operate your ML infrastructure as a managed service: pipeline health monitoring, retraining execution, model registry management, serving infrastructure scaling, incident response, and monthly performance reporting. Ideal for teams that want production-grade MLOps without building an internal ML platform engineering function. For embedding MLOps-managed models into business systems, see our AI integration services for the system-side wiring.
Stack: SLA-Backed Operations | Pipeline Health Monitoring | Retraining Execution | Incident Response | Monthly Reporting
A production ML platform is not a single tool. It is a layered system where data ingestion, feature engineering, training orchestration, model evaluation, serving, and monitoring each have a defined responsibility and a defined interface to the next layer. This is the architecture CMARIX implements for ML platform engineering services engagements.
CMARIX follows a structured, milestone-gated MLOps consulting process that moves from infrastructure audit to a fully operational ML platform, with working deliverables at every stage.
The audit evaluates the maturity level of your current ML stack across five dimensions: pipeline reproducibility, model registry, ML serving, monitoring capabilities, and governance maturity. These dimensions help determine the maturity level and develop a roadmap for future improvements.
Timeline: Week 1-2
Deliverables: Maturity scorecard · Gap analysis · Tool inventory · Bottleneck map · Prioritized roadmap
Your current machine learning ecosystem will be evaluated based on five major aspects: pipeline reproducibility, model registry, serving reliability, monitoring scope, and governance approach. That leads to a maturity assessment with scores and a roadmap to implement the solutions.
Timeline: Weeks 2-4
Deliverables: Architecture blueprint · Tool selection matrix · Integration design · Infrastructure cost estimate
We build the core infrastructure that powers your MLOps platform, integrating automated ML pipelines, feature engineering workflows, centralized model and artifact management, experiment tracking, and scalable model serving. By implementing CI/CD for machine learning, feature stores, model registries, and production serving together, we create a reproducible, version-controlled, and automated ML lifecycle that accelerates deployments while improving reliability and governance.
Timeline: Weeks 4-10
Deliverables: ML pipelines · CI/CD workflows · Feature store · Model registry · Experiment tracking · Model serving infrastructure
We establish comprehensive monitoring, security, and governance controls to keep your ML models reliable, compliant, and production-ready. This includes data and model drift detection, performance monitoring, automated retraining triggers, role-based access controls (RBAC), audit logging, model lineage, and governance policies that ensure every model is secure, traceable, and continuously optimized throughout its lifecycle.
Timeline: Weeks 8-12
Deliverables: Drift monitoring · Alert configuration · KPI dashboards · RBAC and audit logging · Governance documentation
We implement model versioning, experiment lineage, dataset tracking, and RBAC so every model in production is auditable and traceable. We produce compliance documentation for your specific regulatory framework.
Timeline: Week 12-15
Deliverables: Model lineage documentation · Access control configuration · Audit logging · Compliance sign-off
After validation, we deploy your MLOps platform into production with minimal disruption to existing operations. We perform deployment verification, infrastructure validation, and operational readiness checks, and document workflows, runbooks, and best practices. We also provide hands-on knowledge transfer and team enablement to ensure your engineering, data science, and operations teams can confidently manage, maintain, and extend the platform.
Timeline: Weeks 12-15
Deliverables: Production deployment · Validation report · Operational documentation · Runbooks · Team enablement and knowledge transfer
We provide ongoing management of your MLOps platform, including pipeline monitoring, infrastructure maintenance, model lifecycle management, incident response, retraining operations, and continuous platform optimization, backed by SLA-based support.
Timeline: Week 16 onward
Deliverables: Pipeline health monitoring · Incident response · Retraining operations · Performance reports · SLA-backed support
CMARIX develops enterprise-ready machine learning platforms that can be audited, monitored, and secured by engineering leadership, compliance staff, and enterprise architects. Each platform comes equipped with observability tools, security features, and governance documentation that are relevant to your environment.
CMARIX engineers select MLOps tooling based on your existing infrastructure, team size, and model serving requirements. These are the platforms and frameworks we deploy across production MLOps services engagements.
CMARIX delivers MLOps consulting services tuned to the compliance requirements, data environments, and model deployment patterns of each industry.
Healthcare organizations require reliable ML infrastructure for models operating across clinical and operational workflows. CMARIX builds MLOps pipelines for clinical risk prediction, medical imaging inference, patient analytics, and healthcare forecasting. Solutions support automated model training, validation, deployment, monitoring, and version control while accounting for sensitive healthcare data and regulatory requirements. Robust governance and audit trails maintain visibility into model changes, performance, and data lineage throughout the machine learning lifecycle.
Dive Into More
MLOps problems are rarely caused by the models themselves. They stem from missing training pipelines, model registries, drift monitoring, retraining automation, and production governance. CMARIX is the MLOps consultancy company trusted for end-to-end ML platform engineering expertise, cloud-agnostic MLOps implementation, governance and production reliability, and team enablement and managed operations that keep ML systems production-ready.
ML Pipelines in Production
In-House ML & Platform Engineers
Client Retention Rate
Years in Product Engineering
CMARIX structures MLOps consulting services engagements to match your infrastructure maturity and team capacity, from a targeted MLOps audit to a fully managed production ML platform.
What you get:
A structured assessment of your current ML infrastructure maturity, scoring your pipelines, monitoring, governance, and serving against production readiness criteria. Deliverables include a scored gap analysis and a prioritized implementation roadmap.
What you get:
A dedicated ML platform engineering team designs, builds, and operationalizes your target-state MLOps infrastructure end to end, covering training pipelines, model registry, serving layer, monitoring, and governance documentation.
What you get:
CMARIX operates your ML platform as a managed service with defined SLAs: pipeline health monitoring, retraining execution, incident response, model registry management, and monthly performance reporting.
A maturity assessment and roadmap for MLOps costs between USD 8,000 and USD 15,000 in 2 to 3 weeks. Creating an ML Platform costs between USD 40,000 and USD 120,000 in 2 to 5 months. The monthly cost for managed MLOps services is USD 8,000 to USD 25,000 per month based on SLAs. Each project follows milestone-based pricing.
Most MLOps implementations take 8 to 16 weeks, depending on the maturity of your existing infrastructure, the number of models, integration complexity, compliance requirements, and cloud environment. A phased rollout allows critical models to reach production early while the remaining platform components are implemented incrementally.
The most common problems CMARIX resolves are: models that cannot be deployed reliably or reproducibly, no visibility into whether a production model is still accurate, training-serving skew caused by inconsistent feature pipelines, no audit trail for model versions in regulated environments, and data science teams spending more time on infrastructure than on modeling.
A feature store is a centralized system that houses, versions, and serves the features used by your models for training and serving, while maintaining consistency between training and serving. You require a feature store when you have duplicate feature calculations by multiple model teams, training-serving skew, or inefficient serving latency due to expensive feature calculations. CMARIX assesses the need for a feature store before making a recommendation.
Yes. CMARIX builds out MLOps platforms on AWS (SageMaker, EKS), GCP (Vertex AI, GKE), Azure (Azure ML, AKS), and Databricks, and connects to your existing data warehouse, CI/CD pipeline, and identity management system, rather than building an entirely new stack.
Model drift is a condition in which the probability distribution of inputs in the production phase deviates from that used during training, affecting prediction accuracy. CMARIX implements drift detection for both data and predictions, thereby monitoring the distributions of features and outputs in real time.
CMARIX provides model risk management documentation, an audit trail, data set lineage, and access control in alignment with the exact regulatory requirements that apply to you - SR 11-7 in the case of US banking regulation, HIPAA in the case of healthcare, SOC 2 for software companies, and GDPR for operations in Europe. Compliance is built into the platform, not implemented after an audit reveals a problem.
Yes. CMARIX provides SLA-backed managed MLOps services covering pipeline monitoring, model deployment, drift detection, retraining execution, infrastructure maintenance, incident response, security updates, and ongoing platform optimization. This enables your data science team to focus on building models while we operate the production ML platform.
Your unique concepts will be crafted into a remarkable end result by our team.
