Tag: gcp

A production-oriented Cloud Run and vLLM design that keeps GPT-OSS private, separates routing from inference, and makes model loading, readiness, concurrency, and rollback explicit.

Reader outcome: Produced a deployable service contract for private OpenAI-compatible inference with IAM authentication, pinned artifacts, measured concurrency, and a scale-to-zero operating path.

When a SaaS product should graduate from a flexible Python-first backend into Go, gRPC, Cloud Run, and Google Cloud service boundaries.

Reader outcome: Mapped a Go and gRPC adoption path for SaaS teams that need stronger service contracts, concurrency, latency discipline, and Google Cloud operations without premature rewrites.

A Dataform and BigQuery case study for turning data contracts, release lanes, validation gates, rollback behavior, and cost checks into one governed promotion path.

Reader outcome: Reader can inspect a sanitized release pattern that makes schema, freshness, cost, and downstream-impact checks part of promotion rather than after-the-fact review.

A sanitized GCP platform case study where compliance, analytics delivery, and ML feature access had to be designed as one release path instead of three disconnected workstreams.

Reader outcome: Reader can inspect a sanitized GCP pattern for governed dataset onboarding, auditability, cost visibility, and promotion rules across analytics and ML use cases.

A production-focused Vertex AI post on turning raw data, BigQuery features, online feature serving, model endpoints, monitoring, and retraining into one governed ML loop instead of another platform checklist.

Reader outcome: Defined a concrete Vertex AI feature-serving loop with source contracts, BigQuery feature views, point-in-time training exports, endpoint serving rules, monitoring thresholds, and retraining triggers.

A Vertex AI architecture map for teams that need to decide which Google Cloud AI services belong in the ML lifecycle, where ownership changes hands, and which older assumptions are now unsafe.

Reader outcome: Gave teams an operating contract for using Vertex AI across data, features, training, deployment, monitoring, and generative AI without confusing a product menu for a production ML system.

A practical deployment guide for running Ollama behind Open WebUI on Cloud Run GPUs without mixing service specs, model storage modes, sidecar startup order, or auth assumptions.

Reader outcome: Clarified Cloud Run GPU sidecar deployment choices so model storage, service YAML, startup ordering, authentication, and billing constraints are explicit before launch.

A practical view of data strategy as the operating discipline that connects business goals, governance, KPIs, platforms, analytics, ML, and AI delivery.

Reader outcome: Connected data roadmaps, governance, KPI design, platform delivery, and stakeholder alignment so analytics and AI initiatives produced measurable business decisions.