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Home/Services/Discipline 07
// Discipline 07

ML in production, done right.

Training pipelines, evaluation suites, model serving, and observability. The infrastructure that makes ML sustainable beyond the first prototype.

// ml_pipeline · prod · v2.4.1
DATAPREPTRAINEVALDEPLOYMONITORrun_id epoch loss val_acc statusexp_284 14 0.142 0.934 ★ bestexp_283 14 0.189 0.901 archivedexp_281 10 0.251 0.872 archivedserving · p50: 42ms · rps: 2.4k
/ 7.1

Training & Experiment Pipelines

Reproducible, version-controlled training pipelines that make every run auditable.

// mlflow · experiment_tracker · 4 runs
run_idepochlossval_accstatus
exp_284140.1420.934★ best
exp_283140.1890.901archived
exp_281100.2510.872archived
exp_27880.3180.841archived

// Details

  • MLflow / W&B experiment tracking
  • DVC data and model versioning
  • Automated retraining triggers
  • Multi-environment config management

// Output formats

DockerPythonYAML configs
/ 7.2

Model Serving & Inference

Low-latency inference APIs with batching, caching, and graceful degradation.

// inference_api · triton · detector_v2
backend ............ Triton + TensorRT
precision ............ FP16 · batch: 32
latency_p50 ............ 42ms p99: 87ms
throughput ............ 2,400 req/s
uptime ............ 99.98% SLA: 99.9%
canary ............ 5% → v3 rollout active

// Details

  • FastAPI / TorchServe / Triton Inference Server
  • ONNX and TensorRT optimization
  • A/B testing and canary deployments
  • GPU and CPU serving strategies

// Output formats

REST APIgRPCDocker
/ 7.3

Monitoring & Observability

Data drift, prediction drift, latency, and error tracking — with alerts before things break.

// monitoring · drift_report · 2026-05-24
data_drift ......... stable PSI: 0.04
pred_drift ......... WARN PSI: 0.14 → alert sent
latency_p50 ......... 42ms SLO: 100ms
error_rate ......... 0.08% SLO: 1%
alert: pred_drift → PagerDuty ✓ oncall notified

// Details

  • Data drift detection (Evidently, Alibi)
  • Prediction distribution monitoring
  • Latency and throughput dashboards
  • Automated alerting pipelines

// Output formats

GrafanaPrometheusJSON logs
/ 7.4

Cloud Infrastructure

Right-sized cloud infrastructure for ML workloads. We configure what the model actually needs.

// infra · gke · ml_cluster · cost_optimised
cluster ............ GKE · 3 node pools
gpu_pool ............ 4× A100 · spot / preemptible
cpu_pool ............ n2-standard-8 · autoscale
storage ............ GCS + PD-SSD
monthly_est ............ $4,200 vs $11k on-demand
IaC ............ Terraform · GitOps ✓

// Details

  • GCP / AWS / Azure ML infrastructure
  • Spot/preemptible instance strategies
  • Cost analysis and optimization
  • Kubernetes-based orchestration

// Output formats

TerraformKubernetes YAMLDocker Compose
// Work with us

Ready to ship? Let's scope it together.

Whether it's labeled data, a fine-tuned model, a RAG pipeline, or an agent running in production — bring us the brief. We'll scope it, price it, and tell you honestly if we're the right team. Inside 48 hours, no commitment.