back_office_ops · workflow
Distributed Training in MLOps: Accelerate MLOps with Distributed Computing for Scalable Machine Learning
Training large ML models on a single machine is often infeasible due to memory and compute limits — a 175-billion-parameter model would take 288 years on a single NVIDIA V100 GPU — and migrating local experiments to distributed environments requires significant code changes to model distribution, dataset splitting, and process group management.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Local experiment development
A data scientist develops experiment code locally and needs to scale it to handle a much larger dataset.
Tools used
TensorFlowPaddlePaddleRaytorchrunHorovodKubernetesNCCLSlurm
Outcome
(not stated)
Results
Time saved288 years
Grounding & classification
Source type: technical build writeup
13 fields verified against source quotes, 2 dropped as unverifiable.
source backedtools describedworkflow describedcycle time reductiontechnical build writeupback office ops