mlops

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:

1. Assessment & strategy — evaluate current maturity, pick patterns, build a roadmap
2. Implementation patterns — guidance for a specific capability (CI/CD, monitoring, etc.)
3. 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"):

1. STOP generating from general knowledge.
2. Load implement-patterns/INSTRUCTIONS.md immediately, passing known context (pattern, maturity, environments).
3. 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.