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What is distributed training in Azure ML?
Updated May 15, 2026
Short answer
Distributed training uses multiple compute nodes or GPUs to accelerate model training.
Deep explanation
Large machine learning models and datasets require significant computational power. Azure ML supports distributed training frameworks such as:
- PyTorch Distributed
- TensorFlow Distributed
- Horovod
- DeepSpeed
Distributed training improves performance by splitting workloads across multiple GPUs or machines.
Techniques include:
- Data parallelism
- Model parallelism
- Pipeline parallelism
Azure ML simplifies orchestration and infrastructure management for distributed workloads.
Real-world example
A pharmaceutical company trains large transformer models on genomic datasets using multi-GPU Azure clusters.
Common mistakes
- Ignoring synchronization overhead, underutilizing GPUs, and using incorrect batch sizes.
Follow-up questions
- What is data parallelism?
- Why is distributed training important?
- What workloads benefit most from GPUs?