AI Training & Inference
Move models from experimentation to useful operation.
Design around the AI lifecycle.
Training and inference place different demands on accelerators, data pipelines, latency, throughput, and software operations.
Training favors sustained throughput and rapid experimentation, while inference may prioritize latency, concurrency, reliability, and cost per request. One roadmap should account for both.
Connect models, data, and delivery.
Experiment profile
Map model scale, precision, dataset size, checkpointing, and experiment frequency to the training environment.
Inference target
Define latency, throughput, concurrency, availability, and deployment location for production serving.
Data and software
Plan ingestion, preparation, frameworks, orchestration, model artifacts, and observability alongside compute.
Map your AI workload.
Map model sizes, data preparation, experiment cadence, and serving targets to an infrastructure plan that supports the full model lifecycle.
Discuss AI infrastructure