Fordel StudiosMLOps that gets models from notebooks to production and keeps them working.
The industry has shifted from training optimization to inference optimization. vLLM, TGI, quantization, and KV cache tuning are where the production work happens now. W&B and MLflow handle experiment tracking. The real challenge is building the full MLOps stack that makes models reproducible, observable, and improvable after they ship.
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.
W&B and MLflow experiment tracking
We configure and integrate W&B or MLflow for experiment tracking and model registry. W&B offers richer visualization and collaboration for teams running many experiments. MLflow excels at artifact management and model lifecycle. We select based on team scale and workflow.
vLLM and TGI inference serving
We deploy vLLM (PagedAttention, continuous batching) or TGI for production LLM serving. Both deliver significantly higher throughput per GPU than naive serving. We tune KV cache configuration, max concurrent requests, and quantization to meet your specific latency and cost targets.
Inference optimization over training optimization
The current frontier of production ML work is inference — not training. We apply INT4/INT8 quantization, continuous batching, speculative decoding where applicable, and KV cache tuning. For classification models, we evaluate ONNX export with TensorRT optimization.
Canary and shadow deployment
We implement staged rollout that validates a new model version against a small percentage of real traffic before full promotion. Shadow mode runs the new model alongside production without affecting users — generating evaluation data before any traffic switches.
Statistical drift monitoring
KS test and Population Stability Index on input feature distributions, chi-squared on prediction class distributions. Statistical drift triggers review before users observe degradation. Reactive monitoring from user complaints is too slow for production ML systems.
- Reproducible training pipeline with W&B or MLflow experiment tracking
- Model registry with documented promotion criteria and rollback procedures
- Inference serving with vLLM, TGI, or FastAPI — optimized for latency and throughput targets
- INT4/INT8 quantization configuration with accuracy verification on eval set
- Drift monitoring with statistical tests and configurable alerting
- Automated retraining pipeline with evaluation gates
Proper MLOps infrastructure reduces the gap between model improvement and user benefit, eliminates production incidents from training/serving inconsistency, and gives teams the observability to make evidence-based decisions about model updates. The inference optimization work has direct cost and latency impact at production scale.
Common questions about this service.
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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