| 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
