Training pipelines, registries, and monitored serving—ML products that behave like software releases.
From experiment to reliable API—versioned models with drift alerts that wake the right owner
We wire notebooks to reproducible pipelines with data snapshots, feature stores optional, and artifact registries—promotion gates require passing offline metrics and integration tests. Serving paths support blue-green and canary with traffic splits; observability captures latency, errors, and prediction drift against baselines. Cost visibility ties GPU hours and inference tokens to teams—FinOps for ML stops surprise cloud bills after launch week.
01 // THE MANDATE
Training pipelines, registries, and monitored serving—ML products that behave like software releases.
We wire notebooks to reproducible pipelines with data snapshots, feature stores optional, and artifact registries—promotion gates require passing offline metrics and integration tests. Serving paths support blue-green and canary with traffic splits; observability captures latency, errors, and prediction drift against baselines.
Cost visibility ties GPU hours and inference tokens to teams—FinOps for ML stops surprise cloud bills after launch week.
02 // ENGINEERING
Development process
Structured phases—from discovery to launch—with clear ownership and handoff points.
Baseline (weeks 1–4)
MVP (weeks 4–14)
Hardening (weeks 12–20)
Scale (weeks 18–26)
Operate (ongoing)
03 // CAPABILITIES
Core Capability Matrix
The building blocks of your solution
Pipelines
Airflow/Kubeflow patterns; schedules.
Data
validation; PII checks optional.
Training
tracked runs; hyperparam search optional.
Registry
model cards; stage promotions.
Serving
REST/gRPC; autoscale; batch optional.
Monitoring
drift; data quality; alerts.
CI/CD
test harness; smoke deploy.
Access
RBAC; secrets vault.
GPU
quotas; spot optional.
API
batch retrain triggers; feature flags.
04 // DELIVERY LIFECYCLE
The strategic roadmap
Milestones and checkpoints—each phase has a clear outcome before the next begins.
Weeks 1–4: Critical models mapped.
Weeks 5–12: First model on platform.
Weeks 13–20: Canary and drift alerts live.
Weeks 21–26: Org standards and training.
Ongoing: Platform upgrades; new hardware profiles.
05 // PRODUCT SCOPING
Choosing your path
Two engagement models—start lean and iterate, or commit to a full platform build from day one.
MVP
Speed & essentialism
Full product
Enterprise maturity
06 // PARTNERSHIP
Why work together
A single accountable partner across strategy, build, and go-live—not a revolving door of vendors.
End-to-end ownership: discovery, architecture, implementation, and launch—with clear communication and production-grade engineering.
- Discovery & alignment
- Systems that scale
- Implementation depth
- Clear comms
07 // CLARITY
Frequently asked
We meet you on EKS/GKE/AKS or managed serverless where simpler.
08 // MORE SOLUTIONS
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Tell me about your product goals and timeline—I'll respond with a clear path forward.