Back office ops · Production
Distributed Training in MLOps: Accelerate MLOps with Distributed Computing for Scalable Machine Learning
The problem
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.
Workflow diagram · grounded in source
1
Local experiment development
trigger
“A typical scenario involves a data scientist developing experiment code locally and needing to scale it to handle a much larger dataset”
2
Distribute the model
integration
“Devices must communicate after each epoch to share gradient calculations or manage model layers across devices. This can be implemented manually using available collective operations or handled by "wrapping" the model in distributed wrap…”
3
Distribute the dataset
integration
“the dataset must be split between devices — and again, this can be done manually or using modules provided by the framework”
4
Process group management
integration
“To manage the number of training entities, communication, and resource allocation for each worker and effectively integrate with the Distributed Model and Dataset implementations”
5
Launch distributed job
output
“Adapt workflows to infrastructure scales — from local workstations to small/moderate-scale clusters (managed via tools like MPI or Ray) and enterprise-grade systems (orchestrated with Kubernetes)”
Reported outcome
(not stated)
Reported metrics
single-GPU training time for 175B parameter model288 years
Reported stack
TensorFlowPaddlePaddleRaytorchrunHorovodKubernetesNCCLSlurm
Source
https://mlops.community/blog/distributed-training-in-mlops-accelerate-mlops-with-distributed-computing-for-scalable-machine-learning
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
TensorFlow, PaddlePaddle, Ray, torchrun, Horovod, Kubernetes, NCCL, Slurm.
What results were reported?
single-GPU training time for 175B parameter model: 288 years (source-reported, not independently verified).
How is this back office ops AI workflow structured?
Local experiment development → Distribute the model → Distribute the dataset → Process group management → Launch distributed job.