Fordel StudiosMLOps that gets models from notebooks to production and keeps them working.
The hard part of production ML is inference, not training. Quantisation, continuous batching, and KV cache tuning are where real cost and latency improvements live for LLM workloads. For classical ML and fine-tuned models, the equivalent is feature consistency and drift instrumentation — without these, model quality silently degrades as the world changes. We instrument both from the start.
MLOps maturity is not a buzzword — it is the specific set of engineering infrastructure that determines whether a model improves or stagnates after it ships. Without experiment tracking, you cannot reproduce last month's best model. Without a model registry and promotion criteria, you do not know which model version is in production or why. Without drift monitoring, you discover degradation from user complaints rather than instrumentation.
The field has also shifted. In 2021, the interesting engineering problem was training optimization — gradient accumulation, mixed precision, distributed training. In 2025, the interesting problem is inference optimization. vLLM's PagedAttention dramatically improves GPU memory utilization for serving. Continuous batching increases throughput. INT4 quantization cuts memory footprint by 4x with task-dependent accuracy trade-offs. KV cache configuration determines latency behavior under load. These are the levers that matter now.
| MLOps concern | Without infrastructure | With MLOps infrastructure |
|---|---|---|
| Experiment reproducibility | Cannot recreate last month's best model | Every run: hyperparams, data version, artifacts logged in W&B or MLflow |
| Model promotion | Manual approval, unclear criteria | Registry with documented evaluation gates and rollback procedures |
| Distribution shift | User complaints trigger investigation | Statistical drift detection alerts before users are affected |
| Inference optimization | Naive serving, unused GPU capacity | vLLM/TGI with continuous batching and quantization tuned to task |
| Retraining cadence | Ad hoc when someone notices degradation | Drift-triggered or scheduled automated retraining with eval gates |
We design ML systems as software engineering artifacts: versioned, reproducible, observable, and deployable. The training pipeline is code — version controlled, tested, and documented. Feature engineering logic is encapsulated in a layer shared between training and serving to prevent skew. Every training run is tracked in W&B or MLflow with hyperparameters, data versions, and evaluation results.
ML system build layers
Every training run logs hyperparameters, data versions, environment specifications, and model artifacts to W&B or MLflow. Any run can be recreated exactly. Model registry workflows enforce promotion criteria — no model promotes to production without documented evaluation results.
Select the right serving stack for your model and volume: vLLM for high-throughput LLM serving, TGI for HuggingFace models, FastAPI for custom model serving. Apply INT8 or INT4 quantization where accuracy trade-offs are acceptable. Tune KV cache, continuous batching, and max concurrent requests to meet P95 latency targets.
Feature computation logic shared between training and serving via a feature store (Feast, Tecton) or shared library. Training/serving skew becomes a code review concern rather than a production mystery.
KS test, Population Stability Index, and chi-squared tests on input feature distributions and output prediction distributions. Configurable alerting thresholds. Degradation triggers review before users observe visible failures.
Drift-triggered or scheduled retraining pipelines. Every retraining run goes through the same evaluation gates as the original model before promotion. No model promotes automatically without documented quality verification.
- 01
W&B and MLflow experiment tracking
We set up W&B or MLflow based on your team's workflow — W&B for richer visualization when you're running many parallel experiments, MLflow when artifact management and model lifecycle integration matter more. Both give you a model registry with versioned artifacts, documented hyperparameters, and a clear promotion path from experiment to staging to production.
- 02
vLLM and TGI inference serving
We deploy vLLM (PagedAttention, continuous batching) or TGI depending on your model family and hosting constraints. Both deliver 3–5x higher throughput per GPU versus a standard FastAPI wrapper. We tune KV cache size, max concurrent requests, and quantization settings against your specific p50/p99 latency targets — not generic defaults.
- 03
Inference optimization over training optimization
For LLMs, we apply INT4/INT8 quantization, continuous batching, and KV cache tuning as baseline. Where the latency budget allows, we evaluate speculative decoding for additional gains. For classification and regression models, we test ONNX export with TensorRT optimization — typically 2–4x latency improvement over PyTorch serving with no accuracy loss on the eval set.
- 04
Canary and shadow deployment
We implement staged rollout that routes a configurable percentage of real traffic to a new model version before full promotion. Shadow mode runs the candidate model in parallel with production — logging predictions without affecting users — so you accumulate real-distribution evaluation data before any traffic shifts. This eliminates the guesswork in model promotion decisions.
- 05
Statistical drift monitoring
We instrument KS tests and Population Stability Index on input feature distributions, plus chi-squared tests on prediction class distributions. Thresholds are configurable per feature based on historical variance — not one-size-fits-all defaults. Drift triggers automated review before degradation reaches users; waiting for complaint-driven feedback is too slow for production ML systems.
- Reproducible training pipeline with W&B or MLflow tracking
- Model registry with promotion criteria and rollback procedures
- vLLM or TGI serving tuned to your latency and cost targets
- INT4/INT8 quantization with eval-set accuracy verification
- Statistical drift monitoring with KS test and PSI alerting
- Automated retraining pipeline with evaluation gates
Properly tuned inference serving typically reduces GPU spend 40–60% versus naive deployments at equivalent throughput. Statistical drift monitoring catches distribution shift 2–4 weeks earlier than reactive monitoring, which is typically the difference between a silent degradation and a visible incident.
Frequently
asked questions
W&B or MLflow — which should we use?
MLflow excels at artifact management and model lifecycle — it is primarily a model registry and experiment store that is easy to self-host. W&B offers richer visualization, better collaborative features, and stronger real-time monitoring. For small teams running few experiments, MLflow alone is often sufficient. For teams with multiple researchers iterating quickly on experiments, W&B's collaboration features are worth the cost.
What is the inference optimization work that matters now?
vLLM and TGI for serving throughput. INT8 quantization for memory reduction with minimal accuracy loss on most tasks. INT4 for aggressive memory reduction where some accuracy trade-off is acceptable. KV cache configuration for latency behavior under concurrent load. Continuous batching to maximize GPU utilization. These are the levers with the biggest cost and latency impact for LLM serving in production.
Managed ML platforms or self-hosted Kubeflow?
Managed platforms (SageMaker Pipelines, Vertex AI, Azure ML) reduce operational overhead significantly and are appropriate for most production workloads. Self-hosted Kubeflow makes sense when you have existing Kubernetes infrastructure, specific customization requirements, or data residency constraints that managed platforms cannot accommodate. The operational cost of running Kubeflow is real and ongoing.
How do you handle models that need frequent retraining?
We build automated retraining pipelines with configurable triggers: scheduled (weekly, monthly), drift-triggered (when monitoring detects distribution shift above threshold), or event-triggered (new data volume thresholds). Every retraining run goes through the same evaluation gates as the original before promotion. No model promotes automatically without quality verification.
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