Production AI Radar
How-to guides
Adoption playbooks that link radar blips to specific tools, use cases, checklists, and architecture diagrams.
Architecture illustrations
Interactive architecture diagrams with cloud and tooling marks — hover a node, click to focus a step, use ←/→ when the diagram is focused.
LLM gateway · cost · eval Hover a node · click to focus · ←/→ steps
Apps → tagged gateway → AWS / Azure / GCP providers or self-host. Cost tags feed FinOps; sampled traffic closes the eval loop.
Tagged ingress, provider routing, FinOps, and eval sampling.
deploy LiteLLM with per-team cost tagsinstrument AI workloads with OpenTelemetryself-host inference with vLLMAI landing zone Hover a node · click to focus · ←/→ steps
Multi-cloud org → network → identity → Terraform modules. Policy and cost gates sit on every plan (AWS · Azure · GCP).
Accounts, network, identity, and IaC modules with plan gates.
build AI landing zones with TerraformRAG pipeline · eval gates Hover a node · click to focus · ←/→ steps
Ingest → embed → index → retrieve → generate. Offline + online eval gates block regressions before promote.
Ingest through retrieval with offline and online quality gates.
build a RAG eval harness with Langfuse + Ragasrun production vector search with Qdrant or pgvectortrace and debug LLM apps with Arize PhoenixRegistry → canary → rollback Hover a node · click to focus · ←/→ steps
Promote only after eval. Canary a traffic slice on K8s; auto-rollback on SLO breach; GitOps is the audit trail.
Eval-gated promotion with canary traffic and GitOps rollback.
set up MLflow registry with eval gatesGitOps ML manifests with Argo CDserve models with BentoML and canary releasesGuardrails as code Hover a node · click to focus · ←/→ steps
Versioned rails in Git. Input → dialog → output checks; high-severity detections fail closed before the provider.
Versioned input/output rails with CI red-team corpus.
ship LLM guardrails as code with NeMo / Guardrails AIdefend against prompt injection in RAG and toolsFinOps · GPU & LLM metering Hover a node · click to focus · ←/→ steps
Labels on every GPU job + gateway tags on every token. FOCUS-aligned showback across AWS / Azure / GCP.
Labeled GPU hours and gateway tokens into FOCUS showback.
attribute GPU spend with Kubecost and labelsstandardize AI cloud cost with FOCUSAgent harness · approvals Hover a node · click to focus · ←/→ steps
Spec → allowlisted tools → model loop. Writes pause for human approval; MCP goes through an allowlist gateway.
Allowlisted tools, human write gates, MCP allowlist.
deploy an MCP allowlist gatewayrun durable agent workflows with Temporalbuild an agent tool harness with LangGraph approvals
All guides
- AdoptMLOps14 mindiagram
How to set up MLflow registry with eval gates
Version models, block promotion without passing offline evals, and leave an audit trail procurement can review.
Read guide - AdoptLLMOps12 mindiagram
How to deploy LiteLLM with per-team cost tags
Single LLM ingress with budgets, routing, and attribution — stops shadow API keys and makes FinOps possible.
Read guide - TrialLLMOps16 mindiagram
How to build a RAG eval harness with Langfuse + Ragas
Golden-set evals for retrieval quality and faithfulness before every RAG release — not after customer complaints.
Read guide - TrialFinOps10 mindiagram
How to attribute GPU spend with Kubecost and labels
Per-team GPU showback before the CFO cuts your cluster — tags first, tools second.
Read guide - AdoptGovernance11 min
How to redact PII before LLM calls with Presidio
Scan and anonymize sensitive fields at the inference boundary — required for DACH B2B customer data.
Read guide - TrialPlatform & DevEx13 mindiagram
How to GitOps ML manifests with Argo CD
Declarative model deploys with PR review, rollback via revert, and audit trail for regulated releases.
Read guide - TrialPlatform & DevEx14 min
How to ship one AI golden path with Backstage
Opinionated template for deploy + observe + rollback — stop every team inventing a different pipeline.
Read guide - AdoptObservability12 mindiagram
How to instrument AI workloads with OpenTelemetry
Model-level traces and metrics — not just pod CPU — so you catch quality degradation before customers do.
Read guide - AssessGovernance16 min
How to assemble EU AI Act evidence from engineering artifacts
Turn logs, evals, and registry history into Annex IV-style documentation — without legal-only checkbox theater.
Read guide - TrialMLOps13 min
How to pilot Feast for training/serving parity
Centralize features when multiple models share logic — avoid training-serving skew that silently drops accuracy.
Read guide - TrialGovernance12 mindiagram
How to deploy an MCP allowlist gateway
Inventory agent tools, allowlist approved MCP servers, and log every invocation — before prod data is in scope.
Read guide - TrialPlatform & DevEx15 mindiagram
How to run durable agent workflows with Temporal
Long-running agent jobs with retries, checkpoints, and human approval — not fire-and-forget scripts.
Read guide - AdoptPlatform & DevEx18 mindiagram
How to build AI landing zones with Terraform
Account structure, network, identity, and IaC guardrails so AI workloads land in a governed, EU-ready foundation — not a sandbox that became production.
Read guide - TrialLLMOps15 mindiagram
How to run production vector search with Qdrant or pgvector
Choose and operate a vector store with backups, tenancy, and eval gates — RAG quality depends on the index as much as the prompt.
Read guide - TrialGovernance14 mindiagram
How to ship LLM guardrails as code with NeMo / Guardrails AI
Versioned input/output rails, jailbreak blocks, and topic controls in CI — not a prompt paragraph that drifts every week.
Read guide - TrialLLMOps14 mindiagram
How to self-host inference with vLLM
OpenAI-compatible self-hosted serving with batching, GPU utilization, and a path that still goes through your gateway for tags and policy.
Read guide - TrialMLOps12 min
How to version datasets and models with DVC
Reproducible training pipelines with data hashes in Git — so ‘what trained this model?’ has a one-line answer.
Read guide - TrialMLOps13 mindiagram
How to serve models with BentoML and canary releases
Package, version, and canary-deploy models with a clear rollback path — registry promotion becomes a serving event.
Read guide - TrialMLOps12 min
How to orchestrate ML pipelines with Prefect
Scheduled, observable training and batch inference flows with retries — replace brittle cron and notebook ‘Run All’.
Read guide - TrialObservability11 mindiagram
How to trace and debug LLM apps with Arize Phoenix
Open instrumentation for spans, retrieval, and eval overlays — find why a RAG answer failed in minutes, not days.
Read guide - TrialFinOps10 mindiagram
How to cut LLM cost with Redis semantic cache
Cache near-duplicate prompts with embedding similarity — big savings on FAQ-like traffic without serving stale answers blindly.
Read guide - AssessFinOps11 mindiagram
How to standardize AI cloud cost with FOCUS
Export and map spend to the FinOps Open Cost and Usage Specification so AI GPU and LLM lines land in one finance-ready model.
Read guide - TrialPlatform & DevEx12 mindiagram
How to GitOps ML workloads with Flux
Flux-based continuous delivery for model services — image automation, progressive delivery, and audit-friendly Git history.
Read guide - TrialLLMOps15 mindiagram
How to build an agent tool harness with LangGraph approvals
Bounded agent graphs with tool allowlists and human-in-the-loop nodes — production agents that cannot freestyle into prod writes.
Read guide - TrialLLMOps12 mindiagram
How to canary prompt and model releases
Ship prompt/model changes to a traffic slice with eval and business gates — same discipline as binary canaries, for LLM apps.
Read guide - TrialGovernance13 mindiagram
How to defend against prompt injection in RAG and tools
Layered defenses for untrusted documents and tool outputs — assume retrieved text is hostile.
Read guide