| name | mlops |
| title | Plan and run MLOps |
| summary | Router for MLOps work on Snowflake — assess maturity, pick promotion patterns, and implement CI/CD, monitoring, and governance. |
| description | Use when a developer or data engineer wants to assess MLOps maturity, design a promotion strategy (Code/Model/Hybrid), or implement MLOps capabilities (CI/CD, monitoring, retraining, governance) on Snowflake for traditional ML or LLM/GenAI workloads. Triggers: mlops, mlops maturity, mlops assessment, mlops strategy, mlops pattern, mlops framework, model promotion, ml ci/cd, ml monitoring, llmops, rag pipeline ops, fine-tuning ops. |
| prompt | Help me set up MLOps on Snowflake for my ML project. |
| language | en |
| status | Published |
| author | Snowflake Solutions Team |
| type | snowflake |
| tools | ["snowflake_sql_execute","Bash","Read","Write","Edit","Glob","Grep"] |
Plan and run MLOps
Overview
Router skill for operationalizing ML and LLM/GenAI workloads on Snowflake. It covers the process and governance layer — when to promote, what gates to enforce, what to monitor, how to roll back. It does not cover SDK-level code (model registration, feature store APIs, training loops) — that belongs to the machine-learning skill.
This skill applies to traditional ML and GenAI (prompt management, RAG, fine-tuning, agentic apps). There is no separate "LLMOps" — LLM operationalization is part of MLOps with workload-specific adaptations.
Scope split
| Question | Owner |
|---|
| When should I promote a model? What gates must it pass? | mlops |
| How do I register a model or deploy an endpoint? (code) | machine-learning |
| What should I monitor after deployment? When to roll back? | mlops |
| How do I set up Feature Store / Cortex Search? (code) | machine-learning |
| How should I govern Feature Store / Registry across environments? | mlops |
| How do I train / fine-tune / build RAG? (code) | machine-learning |
| How should I operationalize training across environments? | mlops |
Platform constraint: All recommendations assume Snowflake as the platform (Model Registry, Feature Store, Cortex AI, Snowpark, Tasks/Streams). Do not propose third-party platforms unless the user explicitly asks.
Explain before asking: Always introduce concepts (maturity levels L0–L3, promotion patterns, capability dimensions) before asking the user to make decisions about them. Do not assume prior knowledge.
Sub-flows
implement-patterns/INSTRUCTIONS.md — implementation playbooks for promotion, CI/CD, monitoring, governance (includes maturity assessment as part of the pattern selection workflow)
Workflow
Step 1: Detect intent
Ask the user which path they need:
- Assessment & strategy — evaluate current maturity, pick patterns, build a roadmap
- Implementation patterns — guidance for a specific capability (CI/CD, monitoring, etc.)
- Full setup — end-to-end MLOps design from scratch
Step 2: Route
| Intent | Route |
|---|
| ASSESS — "assess maturity", "gap analysis", "roadmap", "where are we" | Load implement-patterns/INSTRUCTIONS.md — start with promotion pattern determination |
| PATTERNS — "promotion pattern", "ci/cd", "monitoring", "retraining", "feature store governance", "RAG pipeline ops", "LLM monitoring" | Load implement-patterns/INSTRUCTIONS.md |
| FULL SETUP — "setup mlops from scratch", "end to end" | Load implement-patterns/INSTRUCTIONS.md — start with promotion pattern determination, then work through capabilities per priority |
⚠️ STOPPING POINT: Before loading implement-patterns/INSTRUCTIONS.md, the user MUST have an explicit promotion pattern (Code / Model / Hybrid). If unknown, run the decision tree (ask about team structure, artifact type, deployment frequency). Do not generate implementation guidance without it.
Step 3: Per-message intent re-evaluation
On every user message — not just the first — re-check intent. If the user shifts to implementation ("start with X", "let's build", "show me the code", "what SQL do I need"):
- STOP generating from general knowledge.
- Load
implement-patterns/INSTRUCTIONS.md immediately, passing known context (pattern, maturity, environments).
- If promotion pattern is unknown, determine it briefly before loading.
Common Mistakes
- Generating implementation code from general knowledge instead of loading
implement-patterns/INSTRUCTIONS.md.
- Skipping promotion-pattern selection and producing pattern-agnostic recommendations (they will be wrong).
- Treating LLM/GenAI as a separate "LLMOps" track instead of a workload variant.
- Recommending non-Snowflake tools (SageMaker, Vertex, Databricks, MLflow) when the user did not ask.
- Answering "how do I register a model" inside this skill — that's
machine-learning.
- Asking the user to choose between L1 and L2 without first explaining what the levels mean.
Red Flags
Refuse these rationalizations:
- "The user seems to know what they want, I'll skip the promotion-pattern question." — No. Pattern is a hard prerequisite.
- "I'll generate the CI/CD pipeline from memory, faster than loading the sub-flow." — No. Sub-flow content is curated and tested; general-knowledge output drifts.
- "They asked about MLflow, I'll just answer." — Only if they explicitly asked. Default is Snowflake-native.
- "The roadmap is obvious, I'll skip the assessment." — No. Maturity baseline drives sequencing.
- "They want to start implementing, I don't need to re-check intent each turn." — Re-evaluate every message.
Stopping Points
- Step 2 — wait for explicit promotion pattern (Code / Model / Hybrid) before loading
implement-patterns/INSTRUCTIONS.md. If unknown, run decision tree or full assessment first.
Output
- Assessment route: maturity scorecard + prioritized roadmap.
- Patterns route: implementation playbook for the selected capability.
- Full setup: complete architecture with sequenced implementation plan.
