MLOps · Infrastructure · Production ML

Production ML Infrastructure
That Actually Scales

From zero to production in weeks, not months. We build, deploy, and manage enterprise-grade MLOps platforms so you can focus on model innovation.

MLOps Capabilities

ML Pipeline Orchestration

End-to-end ML pipeline automation from data ingestion to model deployment.

Data Validation & VersioningFeature Engineering PipelinesDistributed Model Training+5

Model Serving & Inference

High-performance model serving with autoscaling and canary deployments.

REST/gRPC EndpointsBatch Inference PipelinesStream Processing+5

Model Monitoring & Observability

Real-time monitoring for data drift, concept drift, and model performance.

Data Drift DetectionConcept Drift AlertsPerformance Metrics+5

ML Experiment Tracking

Comprehensive experiment tracking and model registry.

Experiment LoggingModel RegistryArtifact Storage+5

Feature Store

Centralized feature management for training and serving.

Feature EngineeringOnline/Offline StorePoint-in-Time Correctness+5

Model Governance & Compliance

End-to-end governance for regulated ML deployments.

Model Lineage TrackingApproval WorkflowsAudit Trails+5

Infrastructure & Platform

Kubernetes Orchestration

Production-grade Kubernetes clusters with auto-scaling and self-healing.

Cluster ManagementHorizontal/Vertical AutoscalingService Mesh (Istio/Linkerd)+5

Cloud Infrastructure

Multi-cloud and hybrid infrastructure automation.

AWS/Azure/GCPInfrastructure as CodeTerraform/CloudFormation+5

Data Infrastructure

Scalable data lakes, warehouses, and streaming platforms.

Data Lakes (S3/ADLS)Data WarehousesStream Processing (Kafka)+5

CI/CD for ML

Continuous integration and delivery for ML systems.

Pipeline AutomationTesting FrameworksContainer Registry+5

Security & Compliance

Zero-trust security for ML infrastructure.

Zero Trust ArchitectureNetwork PoliciesPod Security+5

Disaster Recovery

Business continuity and disaster recovery for ML systems.

Backup StrategiesCross-region ReplicationAutomated Failover+5

Success Stories

Financial Services Company

Reduced model deployment time from weeks to hours with automated MLOps pipelines.

FinTechView case study

Healthcare AI Platform

Scaled from 5 to 50+ models in production with robust monitoring and governance.

HealthcareView case study

E-commerce Recommendation Engine

Achieved 99.99% uptime with multi-region inference serving.

RetailView case study

Autonomous Vehicle Startup

Built petabyte-scale data infrastructure for training and simulation.

AutomotiveView case study

Managed Services

Sirraya MLOps Platform

Fully managed ML platform with auto-scaling and monitoring

End-to-end ML pipeline management
Automated model deployment
Built-in monitoring

Kubernetes as a Service

Managed K8s clusters with enterprise support

Fully managed control plane
Auto-scaling node pools
Integrated monitoring

Data Pipeline Service

Serverless data processing and orchestration

Event-driven architecture
Managed Kafka/Spark
Data quality checks

Model Serving Gateway

Global model inference with edge caching

Multi-region deployment
Automatic failover
Edge caching

Why Leading Teams Choose Sirraya

We don't just consult — we build, deploy, and manage production ML infrastructure.

Faster Time to Production

From months to weeks with our battle-tested platforms

Enterprise Security

Zero-trust, compliant with SOC2, HIPAA, GDPR

Dedicated Support

24/7 engineering support with SLA guarantees

Ready to Scale Your ML Infrastructure?

Let's discuss your challenges and build a roadmap to production.

No commitment. No sales pitch. Just expert advice.