⚙️ Edge#183: Data vs Model Parallelism in Distributed Training
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Enjoy the learning! 💡 ML Concept of the Day: Data vs. Model ParallelismThe core principle of distributed training in ML models relies on dividing the workload between multiple nodes that perform different training tasks. From an architectural standpoint, there are two fundamental approaches to distributed training: data and model parallelism.
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