Model development and scientific research bring their own infrastructure requirements. We help translate compute, data, and execution needs into platforms your team can use and operate.
Discuss your infrastructure needsA successful experiment can expose the next infrastructure problem: jobs that exceed a workstation, inconsistent execution environments, expensive idle GPUs, or a model that needs a dependable serving environment. We help define and implement the next step.
Plan compute around model size, concurrency, memory, and latency requirements. We help evaluate managed services and self-hosted options, configure GPU-capable environments, and design scaling and monitoring around the workload.
What you take awayA deployment architecture, capacity assumptions, and agreed serving infrastructure.
Build environments for computational pipelines and large job queues. We work with AWS Batch, cloud HPC clusters, containerized execution, storage throughput, and job scheduling to support repeatable scientific and bioinformatics workloads.
What you take awayConfigured compute environments, job execution paths, and operating documentation.
Replace inconsistent individual setups with defined environments and appropriate data access. We help configure platforms such as SageMaker Studio, workload identities, storage permissions, and reproducible infrastructure for research teams.
What you take awayProvisioned workspaces, access configuration, and onboarding procedures.
Investigate utilization, queue behavior, data movement, and workload bottlenecks before adding capacity. We help evaluate instance types, scaling to zero, interruption-tolerant execution, and monitoring that makes cost and performance easier to compare.
What you take awayWorkload measurements, configuration changes, and a documented capacity plan.
Compute infrastructure matched to the work, with repeatable execution and enough operational visibility to make informed capacity decisions.
Begin with an assessment, a focused investigation, or a scoped project. We agree on the approach before implementation.
Explore ways to work with usYou don’t need to have every requirement worked out. Share the environment, the challenge, and what you need to accomplish.
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