platform · Microsoft
Computer Vision with Azure OpenAI
Computer Vision built on Azure OpenAI, chosen where it genuinely fits, and swapped where it does not.
- Category
- platform
- Vendor
- Microsoft
- Alternatives we also use
- 7
Why Azure OpenAI for this
Edge deployment matters more than model size in most plants, the network is unreliable and the decision has to happen in milliseconds. We build for the edge first.
Azure OpenAI is strongest at enterprise compliance posture and integration with existing Microsoft estates. For computer vision that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: quota management and regional capacity can constrain scaling at short notice. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- OpenAI models under Azure's compliance envelope and enterprise agreements.
- Strongest at
- enterprise compliance posture and integration with existing Microsoft estates
- Trade-off
- quota management and regional capacity can constrain scaling at short notice
- Category
- platform
We are not a reseller for Microsoft and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Data collection protocol and labelling workflow
- Model training against your real conditions and lighting
- Edge deployment with offline tolerance
- Precision and recall reported per defect class
- Integration with MES, PLC or alerting systems
- Retraining pipeline as conditions drift
Questions
How much training data do we need?
It depends on defect variability, but a few hundred well-labelled examples per class is a realistic starting point. We design the collection protocol first so the data you gather is actually usable.
Does it run without internet?
Yes. We deploy at the edge with offline tolerance, syncing results when connectivity returns, essential in most plant environments.
What accuracy can we expect?
We report precision and recall per defect class against a held-out set from your line, rather than a single headline number. The honest figure varies by class and we show which ones are hard.
Alternatives for computer vision
Same capability, different stack. Each page states its own trade-off.
Building with Azure OpenAI?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878