GPU Servers & MLOps

MLOps (Machine Learning Operations) is the set of practices, tools, and infrastructure that enables organisations to develop, deploy, monitor, and maintain machine learning models reliably at scale — analogous to DevOps for software. Without MLOps, data science teams face common problems: experiments are unreproducible, models that work in development fail in production, retraining is manual and error-prone, GPU resources are wasted, and deployed models degrade silently when real-world data shifts. MLOps solves all of these through automation, standardisation, and observability. If your team has trained models that aren't yet in production, or has production models that rarely get updated, MLOps infrastructure is likely the missing piece.

Both have clear use cases. Cloud GPUs (AWS EC2 P4d, GCP A100, Azure NDv4) offer flexibility, no upfront capex, and access to the latest GPU generations — ideal for variable training workloads, teams just starting ML, or organisations needing H100-class hardware without long-term commitment. On-premise GPUs provide significantly lower per-hour cost at sustained utilisation, data sovereignty, no egress fees, and predictable budgeting — better for teams with consistent training workloads consistently high GPU utilisation. We analyse your training job frequency, model sizes, data volumes, and budget constraints to recommend the optimal mix — often a hybrid strategy with on-premise for base load and cloud burst capacity for peaks.

A foundational MLOps stack — covering experiment tracking, a model registry, a basic CI/CD pipeline, and model serving — can be operational in 4–6 weeks for a small team. A comprehensive enterprise MLOps platform including distributed training, automated retraining, production monitoring, feature store, and full governance typically takes 8–16 weeks. We use a phased approach: start with the highest-impact components (usually experiment tracking and model serving), demonstrate value quickly, then build out the remaining layers incrementally without disrupting your existing workflows. We also offer an MLOps audit service that assesses your current state and produces a prioritised roadmap.

Tool selection depends on your team size, budget, existing stack, and specific needs. MLflow is open-source, highly flexible, and integrates well with existing infrastructure — ideal for teams that want full control and don't want vendor lock-in. Weights & Biases provides a superior UI experience, excellent collaboration features, and powerful visualisations — preferred by research-oriented teams and organisations with larger ML budgets. For orchestration, we recommend Airflow or Prefect for general ML pipelines, and Argo Workflows or Kubeflow Pipelines for Kubernetes-native environments. For LLM-specific observability, LangSmith and Arize Phoenix are our primary recommendations. We evaluate your specific situation and recommend the minimum viable toolchain that solves your actual problems.

Yes — GPU cost optimisation is one of the highest-ROI engagements we undertake. Common optimisations we implement include: mixed precision training (FP16/BF16) reducing GPU memory requirements, gradient checkpointing enabling larger batch sizes without additional GPUs, efficient data loading pipelines eliminating GPU idle time during data fetches, spot/preemptible instance strategies cutting GPU costs substantially, model quantisation reducing inference GPU requirements, auto-scaling inference clusters to zero during off-hours, and right-sizing GPU instance types for each workload. Clients typically see a substantial reduction in GPU costs after an optimisation engagement, with a fast payback.