MLOps
MLOps: Deploy and monitor a model through its lifecycle. Review the task, dataset provenance, labels, baseline, model constraints, latency, and cost and produce an MLOps runbook and rollback rules.
--- name: ai-ml-mlops description: Use for mlops when asked to deploy and monitor a model through its lifecycle; produce an MLOps runbook and rollback rules. license: MIT metadata: author: Thrive category: ai-ml --- # MLOps ## When to use Use this skill for mlops when you need to deploy and monitor a model through its lifecycle. The expected result is an MLOps runbook and rollback rules. ## Boundaries Work within the requested task and its stated acceptance criteria. Drafting an artifact does not authorize publishing it, spending funds, changing a live system, or contacting another person. Identify any such action separately before taking it. ## Inputs Inspect the task, dataset provenance, labels, baseline, model constraints, latency, and cost. Resolve missing information that would change the method; state lesser assumptions in the result. ## Method 1. **Diagnose.** Define task, data rights, labeling quality, baseline, latency, cost, and the consequences of a wrong answer. 2. **Decide.** Select an approach only after defining an evaluation set and failure costs. 3. **Produce.** Build an MLOps runbook and rollback rules from the inspected material; keep assumptions distinguishable from observed facts. ## Decision rules - Reserve holdout cases before tuning. Compare model and non-model approaches and inspect error slices, not only averages. - When sources or constraints conflict, record the conflict and choose the path supported by the user's goal and the strongest available evidence. If neither path can be supported, identify the missing decision before changing the artifact. ## Domain rules - Separate training, validation, and test data to avoid leakage. - Report uncertainty, safety limits, and the cases where the model should abstain. ## Verification Check drift, observability, and version traceability. Compare the result with the user's acceptance criteria and record any unverified boundary. Provide an evaluation set, baseline comparison, failure examples, abstention rule, and monitoring trigger. ## Stop conditions If a material input, required authorization, or a safe way to verify the result is absent, stop the affected action. Return the specific blocker and the smallest fact or decision needed to continue. Do not report an unrun check as passed. ## Output Provide an MLOps runbook and rollback rules. Include the decisive evidence and actual verification result. Name any artifact location and unresolved issue that affects its use. <!-- MIT License Copyright (c) 2026 Thrive Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -->