The gap between a working ML model and a production ML system is wider than most teams anticipate. Without robust MLOps infrastructure, models trained in notebooks sit idle, experiments are unreproducible, deployments are fragile, and GPU resources are wasted. ESS ENN Associates bridges this gap.
We build and manage complete ML operations stacks — GPU cluster provisioning, experiment tracking with MLflow and Weights & Biases, automated CI/CD pipelines for model training and deployment, distributed training with FSDP and DeepSpeed, and production monitoring with drift detection. Your data science team focuses on model quality; we ensure the infrastructure delivers it reliably at scale.
Provision and configure GPU clusters on-premise or cloud (AWS EC2, GCP, Azure) with NVIDIA GPU Operator, Kubernetes, and CUDA optimisation. Multi-GPU and multi-node setup with NVLink/InfiniBand, GPU health monitoring, memory management, and automated job scheduling with SLURM or Kubernetes batch processing.
Implement MLflow, Weights & Biases, or Neptune.ai for comprehensive experiment tracking — logging hyperparameters, metrics, datasets, code versions, and model artefacts. Build model registries with automated staging and promotion workflows, ensuring every production model is fully reproducible and auditable.
Automate the full ML lifecycle — data validation, feature engineering, model training, evaluation, and deployment — using GitHub Actions, GitLab CI, Jenkins, or Argo Workflows. Implement automated regression testing, A/B deployment, canary releases, and rollback mechanisms so model updates ship safely and frequently.
Scale LLM fine-tuning and model training across multiple GPUs and nodes using PyTorch FSDP, DeepSpeed ZeRO, Megatron-LM, and Ray Train. Optimise gradient checkpointing, mixed precision training, and data parallelism to maximise GPU utilisation and minimise training time and cost for large-scale models.
Deploy models at scale using TorchServe, Triton Inference Server, BentoML, Ray Serve, or vLLM for LLMs. Implement batching, model caching, quantisation, TensorRT/ONNX conversion, and auto-scaling to achieve low latency and high throughput with minimal GPU cost per inference.
Monitor model performance, data drift, concept drift, and system health in production using Evidently AI, Arize AI, WhyLabs, or custom dashboards on Grafana. Automated alerting for performance degradation, data distribution shifts, and prediction anomalies — with LLM-specific observability via LangSmith or Arize Phoenix.
Organisations that invest in MLOps infrastructure see dramatic improvements in the speed, reliability, and business impact of their AI programmes — turning data science from experimental to production-grade.
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 over 40% 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 by 50%, gradient checkpointing enabling larger batch sizes without additional GPUs, efficient data loading pipelines eliminating GPU idle time during data fetches, spot/preemptible instance strategies reducing GPU costs by 60–80%, 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 40–70% reduction in GPU costs following an optimisation engagement, with payback in the first month.
ESS ENN Associates builds and manages the MLOps infrastructure your team needs to move from model experiments to production AI systems — reliably, efficiently, and at scale. Let our 1,500+ engineer Chandigarh.IT consortium handle the operational complexity while your team focuses on model innovation.
