MLOps & Model
Management
CI/CD for ML, model registries, automated evaluation gates, scalable serving, and continuous monitoring—so your models actually run in production.
- 100% IP Ownership
- Automated ML Pipelines
- Regression-Proof Deploys
What You Get With Zigron
ML infrastructure that gets models from notebooks to production—and keeps them running.
Automated Training Pipelines
End-to-end ML pipelines that retrain, evaluate, and promote models without manual intervention.
Model Registry & Versioning
Central registry with artifact versioning, lineage tracking, and approval workflows.
Automated Evaluation Gates
Quality gates that prevent regressions—models only deploy if they beat the current production baseline.
Scalable Serving Patterns
Batch, real-time, and streaming inference patterns with auto-scaling, load balancing, and caching.
Production Monitoring
Drift detection, performance degradation alerts, data quality checks, and cost tracking dashboards.
Audit & Compliance
Audit-ready decision logs, model lineage, and governance controls aligned to NIST AI RMF.
Who Is This For?
Teams with great models stuck in notebooks.
Model Won't Deploy
Problem
Data science team trained a great model but can't get it into production reliably.
Solution Approach
MLOps pipeline with containerized training, automated evaluation, model registry, and one-click deployment with rollback.
Outcome
Model deployment time reduced from weeks to hours.
Silent Model Failures
Problem
Production models degrading without anyone noticing until business metrics suffer.
Solution Approach
Monitoring stack with drift detection, performance alerts, data quality checks, and automated retraining triggers.
Outcome
95% reduction in time-to-detect model degradation.
Regulated ML Systems
Problem
Need audit trails and traceability for every model decision in regulated environments.
Solution Approach
Complete model lineage, versioned artifacts, decision logging, and governance controls for compliance.
Outcome
Passed ML compliance audit on first submission.
How We Deliver Excellence
Assess
Evaluate current ML workflow, infra, deployment constraints, and compliance requirements
Design
Define pipeline architecture, serving patterns, monitoring strategy, and governance controls
Build
Implement training pipelines, model registry, serving infrastructure, and CI/CD automation
Validate
Test evaluation gates, rollback mechanisms, monitoring alerts, and load handling
Operate
Production handover with runbooks, incident playbooks, and ongoing optimization
Flexible Engagement Models
Whether you need a Full MLOps Platform Build or Model Deployment Consulting, we adapt to your ML maturity level.
Technical Approach
End-to-end model lifecycle from training to production monitoring.
Data
Pipelines & Features
Training
Experiments & Eval
Registry
Versions & Artifacts
Serving
APIs & Batch
Monitoring
Drift & Alerts
Automation
End-to-end pipelines with zero manual steps.
Quality Gates
Models deploy only when they prove they're better.
Observability
Every model metric tracked, alerted, and dashboarded.
Governance
Audit trails and compliance baked into the pipeline.
Tools & Technologies
Best-in-class tools for ML pipelines, serving, and monitoring.
ML Platforms
Serving & Infra
Monitoring & Ops
Success Stories
AI Solar Tracking Optimization
Services: ML Pipeline, Model Registry, Edge Deployment
Result: Automated retraining pipeline with zero-downtime model updates.
TerraSmart Solar MLOps Pipeline
Services: Automated Retraining, Monitoring, Drift Detection
Result: 30% faster deployment with continuous model updates across sites.
Abode Smart Home Model Ops
Services: Model Serving, Quality Gates, A/B Testing
Result: 99.99% uptime with automated model lifecycle management.
Frequently Asked Questions
Ready to Productionize Your Models?
Tell us about your ML deployment challenges. Our engineers will build the infrastructure that turns experiments into reliable production systems.