Get Your ML Models Into Production — And Keep Them There
Building a model is the easy part. Deploying it reliably, monitoring for drift, managing retraining, and maintaining compliance in production — that's where most teams struggle. We solve exactly that.
Trusted by innovative teams worldwide
MLOps & AI Engineering Certifications
Our MLOps engineers combine ML expertise with production engineering skills — certified across leading platforms.
Production ML — From Model Registry to Real-Time Monitoring
We handle the infrastructure that turns experiments into reliable, monitored production systems — so your data scientists can focus on model quality.
Model Packaging & Registry
Standardized model packaging with versioning, lineage tracking, and approval workflows. MLflow, SageMaker Model Registry, or Vertex AI — configured for your stack and governance needs.
Production Deployment
Real-time inference endpoints, batch prediction pipelines, and edge deployment — with blue-green rollouts, A/B testing support, and automatic rollback if model performance degrades.
Model Monitoring & Drift Detection
Real-time monitoring of prediction quality, data drift, concept drift, and feature distribution shifts — with automated alerts when model performance drops below defined thresholds.
Automated Retraining Pipelines
Event-driven and scheduled retraining workflows that trigger on drift detection, new data availability, or time-based schedules — with automated validation and promotion gates.
Feature Store Implementation
Centralized feature stores (Feast, SageMaker Feature Store, or Vertex) providing consistent, versioned features for training and serving — eliminating training-serving skew.
ML Governance & Compliance
Model cards, explainability reports, bias audits, and full lineage tracking — meeting regulatory requirements for ML models in financial services and healthcare.
Your Best Model Is Useless If It Never Reaches Production
We bridge the gap between notebook and production. Book a free MLOps assessment.
Models should serve customers, not sit in notebooks.
We've deployed fraud detection, credit scoring, and recommendation models that serve millions of predictions daily — with the monitoring and governance that regulated industries require.
MLOps Built for Regulated Industries
Deploying ML in FinTech isn't just an engineering challenge — it's a regulatory one. Our MLOps practice is designed for environments where model decisions must be explainable, auditable, and fair.
Why Data Teams Choose OpenMalo for MLOps
We're not just ML engineers or just DevOps engineers — we're both. That intersection is exactly where MLOps lives.
Let's Get Your Models Into Production
Tell us about your ML challenges — model deployment, monitoring, drift, or governance — and we'll respond with a targeted assessment.
Our Engagement Process
MLOps Assessment
Review of your current ML workflow — training pipelines, deployment process, monitoring gaps, and governance needs. Identification of the highest-impact improvements.
Architecture Design
Target MLOps architecture covering model registry, deployment strategy, monitoring stack, feature store, and retraining pipeline — designed for your scale and compliance requirements.
Platform Build
Infrastructure setup — model registry, serving endpoints, monitoring dashboards, drift detection, and retraining automation — built incrementally with your data team.
Model Deployment
Migrate existing models to the new platform — with A/B testing, canary rollouts, and performance validation. Each model goes live with full monitoring from day one.
Operate & Optimize
Ongoing monitoring, retraining management, and platform optimization — with knowledge transfer to ensure your team can operate the system independently.
What Our Clients Say
“We had 12 models stuck in notebooks because our team couldn't figure out production deployment. OpenMalo built our entire MLOps platform in 8 weeks and all 12 models were serving live traffic within 3 months. The fraud detection model alone saves us $200K/quarter.
“Model drift was killing our credit scoring accuracy and we didn't even know it was happening. OpenMalo's monitoring system detected drift within hours and the automated retraining pipeline fixed it before our risk team noticed. That's exactly what production ML should look like.
“The governance layer was what sold us. Every model has a card, every prediction is traceable, and our compliance team can pull audit reports in minutes. For a regulated lending company, that's not a nice-to-have — it's existential.
$800K/Year Saved Through Real-Time Fraud Detection
MLOps Platform for FraudShield AI
How we built a production MLOps platform that deploys and monitors fraud detection models serving 15M+ predictions daily — with sub-40ms latency, automated drift detection, and full regulatory compliance.
A fraud detection company that couldn't get models to production
FraudShield AI had built powerful fraud detection models in their data science notebooks but couldn't deploy them reliably. Manual deployments took 2 weeks per model, there was no monitoring for model drift, and regulatory auditors were asking for explainability documentation they didn't have.
Our Approach: 2-week assessment, 6-week platform build using SageMaker and MLflow, model migration over 4 weeks, automated monitoring and retraining pipelines, and governance layer with model cards and SHAP-based explainability — all 12 models in production within 12 weeks.
Read Full Case StudyFrequently Asked Questions
We work across AWS SageMaker, Google Vertex AI, Azure ML, and custom Kubernetes-based platforms. For experiment tracking and model registry we commonly use MLflow, Weights & Biases, or platform-native solutions. We recommend based on your existing infrastructure and team familiarity.
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