Vision
Classification, object detection, OCR, content moderation.
Azure Cognitive Services
Ready‑to‑use APIs for Vision, Language, Speech and Decision: reduce time‑to‑value and embed AI into your apps.
Technology Cluster · Back to technologies · AI for Business
Why use them
- Pre‑trained models via REST/SDK with managed scalability.
- Rapid time‑to‑market and predictable costs.
- Built‑in Azure security, compliance and governance.
When to prefer Azure ML
- Domain‑specific accuracy or niche requirements.
- Need for MLOps, experiment tracking and monitoring.
- Strict data, privacy or customization constraints.
Service families
Language (NLP)
Sentiment analysis, entities, text classification, and Q&A.
Speech
Speech‑to‑Text, Text‑to‑Speech, real‑time translation, and speaker verification.
Decision
Recommendations, personalization, and content moderation.
Integration best practices
Data & privacy
Use compliant regions, mask PII and enforce retention policies.
Quality loop
Collect feedback, measure precision/recall and re‑evaluate models regularly.
Cost control
Throttling, caching, batch processing and metrics to avoid spikes and waste.
FAQ
Can I combine multiple cognitive services?
Yes. Vision/Language/Speech often work better together in a pipeline. Keep an eye on end‑to‑end latency and cost.
How do I test before production?
Create separate environments, use synthetic or masked data and automate functional & quality tests for models.