Inference API hardening
Wrap an existing model in a versioned HTTP or gRPC service with auth, schema validation, load tests, and CI deploy hooks aligned to your orchestrator.
Signal Tensor Engineering Consulting
Project-based programming consulting that wires machine learning models into the applications your teams already ship — inference APIs, batch scoring jobs, monitoring hooks, and release checklists grounded in your stack.
Outline your integration scope
Signal Tensor Engineering Consulting Co., Ltd. · Unified Business Number (UBN): 29576101 · Representative: Lin Wei-Ting
Integration readiness checklist
Use this sequence before you sign a statement of work — each item is something we validate during discovery for ML application integration work.
1. Model artifact and runtime
Confirm framework version, serialized weights, and whether inference runs on CPU, GPU, or an external vendor endpoint. We document container base images and memory ceilings so deployment matches training assumptions.
2. Application touchpoints
List every user-facing surface that will call the model: REST handlers, background workers, mobile clients, or ETL stages. We map request schemas, timeouts, and fallback behavior when scores are stale or unavailable.
3. Data contracts
Feature pipelines must match training-time transforms. We trace parquet tables, streaming topics, or on-the-fly featurizers and flag drift risks before code merges.
4. Observability and rollback
Logging fields, metric names, and alert thresholds are specified up front. You get a rollback path — feature flags or route weights — if a release regresses latency or calibration.
We do not sell licenses or subscription tiers. Each engagement is a scoped project with written deliverables, acceptance criteria, and a handoff package your engineers can operate without us in the room.
Production integration work assumes real infrastructure constraints — batch windows, GPU quotas, and network paths between training and serving environments.
Typical project scopes
Pipeline merge
Connect notebooks or batch trainers to scheduled jobs that write scores into warehouses, caches, or search indexes your product already reads.
Release audit
Review an in-flight integration for latency budgets, secret handling, and parity between offline evaluation and live traffic slices.
Questions teams ask before scoping
Do you train models from scratch?
Our projects focus on integration — serving, orchestration, and application wiring. If you need net-new modeling, we can coordinate with your data science group or recommend specialists, but SOW language stays integration-centric.
Which languages and clouds?
Common stacks include Python services with FastAPI or Flask, JVM backends, and TypeScript clients. We have delivered on AWS, GCP, and on-prem Kubernetes; the checklist above is where we nail specifics.
How are projects priced?
Fixed-scope statements of work with milestone billing. We quote after a short discovery call — no published rate card because integration depth varies too widely to label honestly.