Proven SAS to Azure ML Migrations, Fixed Scope
Move your SAS System predictive models and statistical scoring to Azure Machine Learning. Senior engineers, guaranteed equivalence, 6-12 months.
The lowest-cost way off SAS System: our migration tooling automates the repetitive work —you pay senior engineers for judgment, not keystrokes.
How we support SAS System → Azure Machine Learning after migration
- ✓Guaranteed mathematical equivalence for SAS/STAT outputs.
- ✓Direct conversion of SAS Enterprise Miner visual blocks.
- ✓Fixed scope and budget — no surprise costs.
- ✓Senior platform engineers assigned to your project.
- ✓24/7 US-based support for post-migration operations.
Proprietary visual blocks locked in SAS
Your team spent years building SAS Enterprise Miner flows with proprietary visual blocks. Rebuilding them in Azure ML feels impossible.
Inefficient deployment in legacy SAS
Long SAS scoring cycles delay deployment. Meanwhile, Azure ML offers cloud-native pipelines — but converting custom SAS/STAT code is complex.
Locked-in SAS deployment files
Pre-compiled SAS model files can't be moved directly. You need a proven method to preserve logic in Azure.
SAS System → Azure Machine Learning migration — your questions answered
How do you handle proprietary SAS Enterprise Miner blocks?+
Our teams hold deep statistical credentials. We guarantee mathematical equivalence between your legacy SAS outputs and new Azure ML outputs — verified during the migration.
Can we run both SAS and Azure ML during the transition?+
Yes. You keep your legacy platform under vendor support as long as your contract is active. Migrate on your timeline. If it lapses, our third-party support covers most operational issues.
What about custom SAS/STAT regression configurations?+
We also convert pre-compiled SAS proprietary deployment files into cloud-native scoring formats, preserving logic and accuracy.
How do you guarantee the migration scope?+
We start with a free audit of your SAS modeling environment. Then deliver a fixed-scope plan with timeline and cost before any work begins.
Post-Migration MLOps: Keep Models Running Smoothly
After migration, we can set up MLOps architecture, Azure ML model monitoring, and automated retraining pipelines. This keeps your models accurate without manual effort.