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What are Batch Endpoints in Azure ML?
Updated May 15, 2026
Short answer
Batch Endpoints process large volumes of inference requests asynchronously.
Deep explanation
Batch endpoints are optimized for high-throughput, non-real-time prediction workloads. Instead of processing one request at a time, batch endpoints process datasets in bulk.
Typical use cases include:
- Fraud analysis
- Demand forecasting
- Customer segmentation
- Recommendation generation
- Large-scale scoring jobs
Batch endpoints scale efficiently and reduce operational costs compared to real-time APIs.
Real-world example
A bank runs overnight fraud scoring jobs across millions of transactions using batch endpoints.
Common mistakes
- Using online endpoints for massive workloads and not optimizing batch sizes.
Follow-up questions
- When should batch endpoints be used?
- What is the difference between online and batch endpoints?
- Why are batch endpoints cost efficient?