Solutions | Model Fine-Tuning
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.
Book a model scaling discovery call
AWS-funded for qualifying customers
Training + inferenceThe model works. The infrastructure is now the blocker
AI-native companies hire deep scientific and ML talent before they hire DevOps. Training works on a laptop or a university supercomputer. The moment they need to scale it, the engineering gap shows and research velocity collapses.
Training takes weeks
Work that should take hours on AWS takes days or weeks locally. Research velocity collapses and the gap to better-resourced competitors widens every sprint.
FIX
Move training onto an optimized SageMaker / HyperPod setup to compress iteration cycles.
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
Design the inference architecture before launch — latency requirements, autoscaling and cost targets agreed up front.
No infra hire on the 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 it, and hand it back so scientists can run training without an engineer for every job.
A practical path from local training to scalable inference
3 phasescredit applicationtraining pipelineproduction inference
Discovery & credit application
Understand 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 budget
- AWS credit eligibility — ANCLS programme, Hyperpod credits
Output
Credit application + target training/inference architecture.
Training pipeline build
Stand up the SageMaker / HyperPod training pipeline so scientists can fire off runs without engineering each one. Reproducible, version-controlled, cost-aware.
- SageMaker / HyperPod training setup for distributed training runs
- Reproducible training runs — experiment tracking, dataset versioning, artefacts to S3
- Distributed training — data and model parallelism where needed
- Cost-aware compute — spot instances, savings plans, reserved capacity
- Documentation and runbook — scientists can launch and repeat runs from day one
Output
First successful reproducible training run on AWS before inference architecture begins.
Inference at scale
Production hosting of the trained model on Amazon Bedrock (custom imports), SageMaker endpoints, or AWS Trainium/Inferentia (HyperPod). Latency, cost and reliability targets agreed before launch.
- 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.
Discovery & credit application
Understand 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 budget
- AWS credit eligibility — ANCLS programme, Hyperpod credits
Output
Credit application + target training/inference architecture.
Training pipeline build
Stand up the SageMaker / HyperPod training pipeline so scientists can fire off runs without engineering each one. Reproducible, version-controlled, cost-aware.
- SageMaker / HyperPod training setup for distributed training runs
- Reproducible training runs — experiment tracking, dataset versioning, artefacts to S3
- Distributed training — data and model parallelism where needed
- Cost-aware compute — spot instances, savings plans, reserved capacity
- Documentation and runbook — scientists can launch and repeat runs from day one
Output
First successful reproducible training run on AWS before inference architecture begins.
Inference at scale
Production hosting of the trained model on Amazon Bedrock (custom imports), SageMaker endpoints, or AWS Trainium/Inferentia (HyperPod). Latency, cost and reliability targets agreed before launch.
- 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 from the programme
Training in hours, not weeks: Faster experiment cycles on AWS for quicker model improvement.
Inference that scales: Production serving with established latency, reliability and cost targets.
Build cost offset: AWS credits and funding routes identified to reduce build costs.
No DevOps hire needed: A documented, handed-over pipeline scientists can run without an engineer for every job.
From experiment to repeatable training and scalable inference
We don't just provide more compute. We design the full path from dataset and experiment tracking to reproducible runs, cost-aware training and operational inference endpoints — a pipeline your team can operate independently after handover.
The model is no longer just an experiment.
Model fine-tuning becomes urgent when local training, ad hoc compute or manual inference workflows start slowing research velocity and customer growth.
WHAT WE PRIORITISE
Training regime
Model architecture, run length and compute profile
Data flow
Datasets, versioning and artefact storage
Compute strategy
SageMaker / HyperPod, distributed training, cost controls
Inference path
Endpoint type, autoscaling and latency budget
Clinical models need reproducibility before scale.
Health Tech teams cannot move from local experiments to production without controlled datasets, reproducible training runs, privacy-aware infrastructure and inference that is reliable enough for clinical or operational use.
WHAT WE PRIORITISE
- Sensitive data handling
- Approved dataset use
- Reproducible training
- Validation path
- Inference reliability
The model is no longer just an experiment.
Model fine-tuning becomes urgent when local training, ad hoc compute or manual inference workflows start slowing research velocity and customer growth.
WHAT WE PRIORITISE
Training regime
Model architecture, run length and compute profile
Data flow
Datasets, versioning and artefact storage
Compute strategy
SageMaker / HyperPod, distributed training, cost controls
Inference path
Endpoint type, autoscaling and latency budget
Choose the right model scaling 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
Ready to move model training off laptops?
Book a focused 20-minute conversation. We'll help you assess training setups, inference needs, AWS credit eligibility and the infrastructure path that fits.
Why talk to us:
SageMaker / HyperPod pipeline design
Faster training and scalable inference
AWS credit and funding routes
Handover your scientists can actually use
Start with a focused 20-minute conversation about your goals — no pressure, no commitment.