Training-serving skew
Features computed differently in notebooks and production silently degrade live accuracy, and the drift is hard to spot without shared transforms.
PyTorch Deep Learning MLOps ONNX
PyTorch development & production ML engineering
We build, train and ship PyTorch models that survive contact with production. From data pipelines and distributed GPU training to low-latency inference APIs, our engineers cover the full lifecycle. US and EU teams rely on us to turn research notebooks into governed, monitored services — not just one-off experiments. Every deployment is reproducible, observable and compliant by design.
We build, train and ship PyTorch models that survive contact with production. From data pipelines and distributed GPU training to low-latency inference APIs, our engineers cover the full lifecycle. US and EU teams rely on us to turn research notebooks into governed, monitored services — not just one-off experiments. Every deployment is reproducible, observable and compliant by design.
Challenges
Industry challenges we solve
Reproducibility & experiment tracking
Untracked runs, random seeds and data versions make results impossible to reproduce or audit months later.
GPU cost & scaling
Idle GPUs, oversized instances and naive distributed training quietly burn budget while jobs still take too long.
Model drift & monitoring
Without live metrics and drift detection, models decay against shifting data and no one notices until users complain.
Inference latency & optimisation
Research-grade models are often too heavy to serve at target latency and cost without ONNX export, quantisation or distillation.
Data governance & PII in training sets
Personal data leaks into training corpora, breaching GDPR and CCPA and creating models that cannot satisfy erasure requests.
Solutions
Solutions we build
Model training pipelines
We build reproducible PyTorch and Lightning training pipelines with shared feature transforms, distributed multi-GPU support via Ray, and versioned data.
Experiment tracking
MLflow captures parameters, metrics, artefacts and lineage for every run, so results are comparable, reproducible and audit-ready.
Serving & inference APIs
We expose models through TorchServe or FastAPI with batching, autoscaling and health checks behind clean, versioned endpoints.
Optimisation
ONNX export, quantisation and distillation cut model size and latency, letting you hit cost and SLA targets on CPU or GPU.
MLOps CI/CD & monitoring
Automated retraining, evaluation gates and deployment run through CI/CD, with live drift, latency and quality monitoring in production.
Governance & data lineage
We track dataset provenance, enforce PII controls and wire erasure into pipelines so models stay compliant with EU and US rules.
Stack
Technology stack
PyTorch, Lightning, TorchServe, ONNX, CUDA, Hugging Face, MLflow, Ray, FastAPI, Docker, Kubernetes.
Compliance
Compliance & regulations
EU AI Act · GDPR training data · model governance · SOC 2
EU
- EU AI Act — we classify each model by risk tier, maintain the required technical documentation and conformity evidence, and design human-oversight controls into high-risk inference paths.
- GDPR — lawful basis for every training dataset, PII minimisation and pseudonymisation, Art 22 safeguards on automated decisions, and the right to erasure propagated through datasets and retrained models.
- eIDAS / sector rules — where models touch identity, payments or other regulated domains, we align serving and audit trails with the relevant eIDAS and sector-specific obligations.
- NIS2 — for essential and important entities we harden the ML supply chain, secure model and data stores, and add incident-reporting hooks to the inference platform.
US
- NIST AI RMF — we map model risks across the Govern, Map, Measure and Manage functions, with documented evaluation and monitoring for each release.
- HIPAA — where health data is used for training or inference, we enforce PHI segregation, encryption, access controls and signed BAAs across the pipeline.
- SOC 2 — training and serving infrastructure runs under SOC 2 controls with logging, change management and least-privilege access to GPU and data resources.
- CCPA / CPRA — we honour California opt-out and deletion rights, track data provenance, and exclude restricted records from training sets on request.
Cases
Selected PyTorch case studies
Sports Media · Mobile
Media Arena
Cross-platform sports news app and web portal — Telegram-bot CMS instead of a custom admin, Markdown publishing pipeline.
HealthTech · Fitness
MFIT Fitness App
Native iOS & Android fitness-marathon and challenge app — programs, stats, and leaderboards on a Laravel backend, for the US & EU.
HealthTech · Diagnostics
Unilab
Patient app for a 40-city lab network — appointment booking, digital results, 2,500+ tests, scheduling and accounting integrations.
Why YuSMP
Why teams choose YuSMP for PyTorch development
Full lifecycle, one team
From data engineering to GPU training, serving and monitoring, the same senior engineers own the model end to end — no handoff gaps.
Compliance built in
We design for the EU AI Act, GDPR, NIST AI RMF and SOC 2 from the first commit, not as an afterthought before launch.
Production-grade by default
Reproducible pipelines, optimised inference and live monitoring mean your models stay fast, accurate and observable under real load.
FAQ
PyTorch Development FAQ
Should we use PyTorch or TensorFlow?
For most new deep-learning work we recommend PyTorch: its eager execution, debugging experience and ecosystem (Lightning, Hugging Face) make research and iteration faster. TensorFlow still has strengths in some mobile and legacy serving stacks. We are happy to assess your existing assets and pick the framework that minimises risk and total cost.
What is the difference between training and inference infrastructure?
Training is bursty and GPU-heavy — you need powerful, often distributed hardware for hours or days, then release it. Inference is steady and latency-sensitive, optimised for throughput and cost per request. We design them separately so you never pay for idle training GPUs to serve predictions, and we right-size each independently.
What are our options for serving PyTorch models?
Common paths are TorchServe for native PyTorch serving with built-in batching and metrics, or a FastAPI service wrapping the model for tighter control and custom logic. For high throughput we add ONNX Runtime or Triton. We choose based on your latency targets, scale and existing platform.
What is ONNX and when should we optimise models?
ONNX is a portable model format that lets you run PyTorch models on optimised runtimes across hardware. Once a model is accurate enough, we export to ONNX and apply quantisation or distillation to shrink it and cut latency. This typically reduces inference cost substantially with minimal accuracy loss when done carefully.
What MLOps stack do you use?
We standardise on MLflow for experiment tracking and model registry, Ray for distributed workloads, Docker and Kubernetes for deployment, and CI/CD pipelines that gate releases on evaluation metrics. Monitoring covers data drift, prediction quality and latency. The exact tools flex to fit your cloud and existing infrastructure.
How do you control GPU cost?
We use spot or preemptible instances for training, autoscaling and scale-to-zero for inference, mixed-precision training, and model optimisation to fit smaller hardware. We also profile jobs to remove bottlenecks so GPUs finish faster. Together these often cut compute spend significantly without sacrificing throughput.
What does the EU AI Act mean for our ML models?
The Act classifies AI systems by risk and imposes obligations — technical documentation, data governance, human oversight and conformity assessment — mainly on high-risk uses. We help you classify each model, document it correctly, and build oversight and logging into the inference path so you meet the requirements without stalling delivery.
Ready to ship PyTorch models to production?
Response within 1 business day. NDA on request.