MLOps

AI workloads deserve the same
engineering rigour as
production systems. We enforce it.

FalconIO brings Kubernetes-native MLOps infrastructure — GPU scheduling, model serving, pipeline observability, BC Manifests for production AI endpoints, and incident management that understands ML context — to teams who refuse to treat AI infrastructure as a special case.

Kubernetes-Native GPU IDP for ML Workloads BC Manifests for Model Serving
The Problem

AI infrastructure is treated as
an exception to every platform standard.

GPU Nodes Provisioned Manually

GPU compute is hand-provisioned, often over-provisioned, and invisible to the platform observability stack. Nobody knows how much is used, by what, or why.

Model Serving Outside GitOps

Model serving is treated as a special case outside the GitOps delivery model. Updates are manual. Rollbacks require human intervention. No RTO declaration — until a model goes down.

Pipeline Failures Discovered Late

Training pipeline failures are discovered by the data science team, not the platform. Observability stops at node-level GPU utilisation percentage — the least useful signal for ML infrastructure.

How We Handle AI Workloads

ML infrastructure as
a first-class platform citizen.

GPU compute environments are provisioned through the same IDP service catalogue as all other infrastructure — Crossplane compositions for standard GPU cluster requests, Pulumi stacks for complex multi-GPU configurations with conditional resource profiles across hardware generations.

Model serving endpoints on Kubernetes — LLM inference services, embedding servers, classification endpoints — managed via the same FluxCD GitOps delivery pipeline as every other production service.

We have operated LLM inference services, document extraction pipelines, and risk-adjusted scoring models on Kubernetes at production scale. The MLOps capabilities in FalconIO are derived from that operational experience — not from a reference architecture.
GPU scheduling — workloads provisioned via IDP, same Crossplane + Pulumi engine as all infra
GPU in IDP catalogue — data scientists request compute environments via self-service, with policy gates
Dynamic GPU resource constraints — granular control across GPU makes and generations
KEDA for batch inference autoscaling — queue-depth-driven, demand models from ClickHouse
Training pipeline observability — GPU utilisation, memory bandwidth, step throughput in ClickHouse
LLM inference observability — token throughput, queue depth, P99 latency as first-class metrics
Model serving via GitOps — FluxCD delivery for all serving configurations, same as production services
BC Manifests for model serving — production endpoints have declared RTO/RPO and automated failover
ML incidents in native queue — GPU exhaustion, pipeline failures with GPU snapshots auto-attached
Document extraction and processing pipelines — AI pipelines with full observability and retry semantics
ML-Specific Intelligence

GPU is not a black box.
Make it observable.

Standard observability surfaces node-level GPU utilisation as a percentage. FalconIO surfaces GPU memory bandwidth saturation, kernel execution efficiency, model serving latency percentiles, training step throughput, and batch queue depth — correlated with infrastructure resource model. You understand performance, not just utilisation.

GPU Memory BW
Bandwidth saturation per workload
Step Throughput
Training steps per second over time
Queue Depth
Inference queue — KEDA scaling input
P99 Latency
Model serving endpoint tail latency
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