Azure Cognitive Services
Ready‑to‑use APIs for Vision, Language, Speech, and Decision.
Azure AI Technologies
Overview of Azure AI services and common use cases to bring AI into your solutions securely and at scale.
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Core services
Choose pre‑trained components for common tasks or Azure Machine Learning for custom models and MLOps.
Azure Machine Learning
Train, register, deploy and monitor models with pipelines and MLOps.
Integration & Apps
Embed models and APIs into enterprise applications with security, governance and cost control.
Responsible AI
Guidelines and tooling for reliability, privacy and transparency across the lifecycle.
Common use cases
Computer vision
Classification, object detection, OCR, and content moderation for images and video.
Start here: Computer Vision
Language understanding
Sentiment analysis, entity extraction, text classification, and Q&A.
Start here: Language (NLP)
Speech
Speech‑to‑Text, Text‑to‑Speech, real‑time translation, and speaker verification.
Start here: Speech
Decision & recommendations
Recommendations, personalization and content moderation.
Start here: Decision
Custom ML
Tailored model training, AutoML, model registry and managed deployments.
Start here: Azure ML
Enterprise integration
APIs, events and data pipelines to embed AI into processes.
Start here: AI Integration
Suggested paths
1. Prototype
Validate quickly with Cognitive Services on a sample dataset.
2. Scale
Move to Azure ML to train and monitor custom models.
3. Integrate
Expose APIs and embed into workflows with logging and security.
FAQ
What’s the fastest way to start?
Start with pre‑trained Cognitive Services endpoints to validate value. If you need tailored models, migrate workflows to Azure ML.
How do I control costs and governance?
Use separate environments, quotas, centralized logs/metrics and data access policies; monitor drift and schedule retraining.