You've trained the model on a laptop. Now train it at scale
A SageMaker and Hyperpod pipeline for AI-first teams who out-engineer themselves at training time. We stand up the training stack, get inference running at scale, and bring AWS credits that often offset the build entirely.
The Challenge
AI-native companies hire deep scientific and ML talent before they hire DevOps. Training works on a laptop. The moment they need to scale it, the engineering gap shows and research velocity collapses.
Training takes weeks
What should be hours on AWS is days or weeks locally. Research velocity collapses and the gap to better-resourced competitors widens every sprint.
Fix
SageMaker and Hyperpod compress that cycle. A run that takes a week locally takes hours on the right distributed training configuration.
Inference can't scale
The model demoes beautifully on a laptop. Serving real customers under traffic — the team has no idea how to host it at scale without costs spiralling.
Fix
Scope the inference architecture before the training pipeline is built. Autoscaling, latency budgets, and cost-per-call targets are agreed before launch.
No DevOps hire on roadmap
You're 60 people deep without a DevOps engineer. Hiring one costs three months you don't have, and the runway burns in both directions.
Fix
We stand up the stack, document everything, and hand it back. Scientists can fire off training runs without engineering each one.
How We Deliver
Discovery & credit application
Weeks 1–2Understand the model, training regime, inference needs, and data flow. Identify AWS credit and ANCLS programme eligibility and submit application before build.
- •Model architecture and training regime audit
- •Current compute setup — local, university, ad hoc cloud
- •Inference requirements — traffic volumes, latency targets, cost-per-call
- •AWS credit eligibility — ANCLS programme, Hyperpod credits
Output
Credit application submitted and eligibility confirmed before build scope finalised.
Training pipeline build
Weeks 3–10Stand up the SageMaker / Hyperpod training pipeline so scientists can fire off runs without engineering each one. Reproducible, version-controlled, cost-aware.
- •SageMaker / Hyperpod training cluster configuration
- •Reproducible training runs — experiment tracking, dataset versioning, output artefacts to S3
- •Distributed training setup — data and model parallelism where needed
- •Cost-aware compute provisioning — spot instances, savings plans, reserved capacity
- •Documentation and runbook — scientists can run it on day one
Output
First successful reproducible training run on AWS before inference architecture begins.
Inference at scale
Weeks 8–14Production hosting of the trained model. Latency, cost, and reliability targets agreed before launch and validated under load.
- •Inference architecture design — endpoint type, autoscaling, latency budget
- •Endpoints deployed and load tested
- •p50/p95 latency targets validated under realistic traffic
- •Cost-per-call telemetry wired and dashboard live
Output
p95 latency and cost-per-call targets met under load test before go-live.
What You Get
Training in hours, not weeks: The iteration cycle compresses. The model gets better, faster, against actual budget. Research velocity is restored — and then some.
Inference that scales: Production hosting your customers don't notice. p95 latency in the budget. Cost-per-call known and managed.
Build cost largely offset: AWS credits and ANCLS funding typically cover £30–40k+. Sometimes the whole build. The application is part of phase one.
No DevOps hire needed: The pipeline is documented, handed over, and runnable by your scientists. Hire the next scientist, not the DevOps engineer.
Choose Your Engagement
Pipeline Discovery
Two-week scoping. Architecture design, credit application, and target pipeline specification.
- • Model and training regime audit
- • AWS credit application submitted
- • Target pipeline design
Cost
AWS-funded
Duration
2 weeks
Pipeline Build
Training and/or inference build. 4–8 weeks depending on architecture. Credits offset a meaningful share — often most.
- • Training pipeline on SageMaker / Hyperpod
- • Inference endpoints deployed and tested
- • Cost and latency targets validated
- • Full documentation and handover
Cost
Largely offset
Duration
4–8 weeks
Managed AI Ops
Ongoing operation of training and inference — for teams who want humans on the model, not on the platform.
- • Training run monitoring and optimisation
- • Inference endpoint health and cost
- • Monthly credit burn-down reporting
Cost
Monthly retainer
Duration
Monthly
Related success stories
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Mane Contract Services
Generative AI CV analysis on Amazon Bedrock, replacing manual screening.
Ready to train at AWS scale?
Let's stand up the training infrastructure so your scientists can focus on the model.
Why talk to us:
Outcome-driven recommendations
AWS-recognised delivery expertise
Risk-aware AI adoption
Clear next step, not a sales pitch
Start with a focused 20-minute conversation about your goals — no pressure, no commitment.