Train & version
Reproducible training pipelines with versioned data, code, and parameters—every model has a lineage record from day one.
/ IT & data /
MLOps that turns experiments into production
Versioned training, deployment, monitoring, and rollback for AI models—delivered as a platform your data science team actually wants to use.
The problem
Most AI models never leave the notebook
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Pilots don't reach production
Data science ships a notebook, engineering rewrites it for production, and the original feature logic drifts. Most pilots quietly die in the handover.
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No model versioning anyone trusts
Versions live in folder names and Slack messages. When something breaks, no one is sure which model is in production or what data it was trained on.
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Monitoring is alerts on a dashboard
Latency and CPU are watched; drift, performance decay, and segment-level regressions are noticed when a business user complains.
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Rollback is by emergency call
Reverting a model means a war-room call, a manual redeploy, and a postmortem nobody enjoys. There is no first-class rollback path.
How it works
An MLOps platform your team adopts, not avoids
Deploy & route
One-command deployment with shadow, canary, and A/B traffic routing—so new versions prove themselves before taking real traffic.
Monitor & roll back
Drift, performance, and segment-level checks with automated alerts and one-click rollback to a known-good version.
Platform adapts to your cloud, model frameworks, and existing data stack.
MLOps that turns experiments into production
What's included
Everything you needso models go from notebook to production
Training, registry, deployment, monitoring, and rollback—delivered on Synapse as one operating layer for data science and ML engineering.
Training pipelines
Reproducible pipelines with versioned data, code, and parameters—every run captured in lineage.
Model registry
Central registry with versioning, stage promotion, ownership, and approval gates between staging and production.
Deployment automation
One-command batch, real-time, and embedded deployment with environment parity and infra-as-code.
Drift & performance monitoring
Input drift, prediction drift, and outcome-level performance with segment breakdowns and named alert owners.
A/B & shadow traffic
Shadow new versions on real traffic, run A/B tests with statistical guardrails, and graduate winners safely.
Rollback
One-click rollback to the previous known-good version with full audit trail and incident attribution.
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Results
What changes when MLOps is a platform, not a side project
Results vary by context, data maturity, and scope. We scope honestly before we promise precisely.
5–10x
more models reaching production per quarter
Orientative—depends on starting maturity and team size.
Minutes
to deploy a new model version vs. days previously
Orientative—based on platform-enabled teams.
Tracked
drift, performance, and rollback on every production model
How we work
From first model to a platform—without a big-bang program
Assess
Assess
Week 1–2
Inventory current models, deployment pain, monitoring gaps, and platform requirements.
Design
Design
Week 3–5
Define registry model, deployment patterns, monitoring contracts, and rollback policy with data science and engineering.
Build
Build
Week 6–12
Stand up training, registry, deployment, monitoring, and prove on a first real production model.
Operate & scale
Operate & scale
Week 13+
Onboard additional teams and models, expand monitoring coverage, and operate the platform as a service.
Timelines vary by model count, framework diversity, and existing infrastructure.
Go deeper
MLOps, AI, and what to explore next
Get started
Ready to turn pilots into production AI?
No commitment. We start with a scoped session to map your models, gaps, and platform needs.