Run data preparation, classical ML, training, evaluation, batch jobs, and production inference on isolated hardware configured around CPU, memory, storage, network, and verified accelerator demand.
Measure dataset size, feature jobs, training runs, evaluation cycles, batch windows, and inference concurrency before sizing.
Choose CPU, RAM, storage, network, and verified GPU capacity from the stage that constrains throughput or completion time.
Separate preprocessing, checkpoints, artifacts, and service traffic so storage and transfer pressure remain attributable.
Control the OS, drivers, frameworks, containers, monitoring, updates, and recovery process within your own ML stack.
Machine learning is not one workload. Data preparation stresses CPU and memory, training may justify verified accelerator capacity, evaluation rereads shared artifacts, and inference adds concurrency and latency targets. Map each stage to an explicit infrastructure boundary before compute, storage, or network contention dictates the final architecture.
At Melbicom, we provide isolated dedicated hardware with configurable CPU, RAM, storage, bandwidth, and regional placement. Match each role to observed demand, keep data movement attributable, and retain direct control of the operating environment. The ML platform, frameworks, and model operations remain explicitly customer-owned.
Use our ready-to-deploy or custom configurations to assign preparation, training, evaluation, batch, and inference roles to explicit infrastructure and compute capacity. Add nodes only when measured throughput, resilience, or job parallelism requires them, while your team retains ownership of drivers, pipelines, monitoring, releases, and recovery.
Use CPU, memory, storage, and network capacity for cleaning, joins, transforms, and repeatable feature generation.
Run regression, classification, clustering, and forecasting on capacity sized from representative datasets and benchmarks.
Assign verified accelerator capacity when model architecture and framework benchmarks show a clear training benefit.
Separate validation, test sets, and repeated scoring so evaluation demand remains visible beside training jobs.
Reserve capacity for recurring predictions, retraining, reports, and data refreshes within measured batch windows.
Size CPU, memory, network, and verified accelerators from request concurrency, payload size, and latency targets.
Keep checkpoints, datasets, metrics, and model artifacts traceable through customer-owned release and recovery procedures.
Add nodes only when measured parallelism, resilience, or role isolation justifies a distributed operating model.
