ml-engineering
v0.4.6ML-engineering (MLOps) team — agents (ml-platform-architect, training-pipeline-engineer, model-serving-engineer, ml-monitoring-engineer) for the PRODUCTION lifecycle of ML models: the platform/architecture (build-vs-buy, the stack, the train->register->serve->monitor loop), reproducible training pipelines + experiment tracking + a model registry, feature stores and train/serve consistency (avoiding training-serving skew and leakage), model serving (online vs batch, shadow/canary), monitoring (data + concept drift, decay, retraining triggers), and computer-vision MLOps (task->architecture + edge-vs-cloud inference). skills, a decision-tree knowledge bank (serving-pattern + retraining + computer-vision trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Seams: significance -> applied-statistics, data pipelines -> data-platform/data-streaming, LLM/agent apps -> claude-app-engineering, deploy -> devops-cicd/cloud-native-kubernetes. Requires ravenclaude-core@>=0.7.0.
By Matt CorbettLicense: MIT7 GitHub starsUpdated last week
Directory evidence
- Runtimes
- Claude Code
- Parsed components
- 6 skill or MCP entries
- Source updated
- Sep 15, 2026
- Manifest status
- Canonical path parsed
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Install ml-engineering for Claude Code
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install ml-engineering-2@agent-plugin-marketplacePaste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/mcorbett51090/RavenClaudeClone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/ml-engineering/.
Plugin files
├── .claude-plugin/plugin.json├── skills/computer-vision-pipeline/SKILL.md├── skills/feature-store-consistency/SKILL.md├── skills/ml-experiment-tracking/SKILL.md├── skills/model-monitoring/SKILL.md├── skills/model-serving/SKILL.md└── skills/reproducible-training/SKILL.md
Included Skills6
Run the computer-vision MLOps lane end to end: data/annotation -> CV task -> architecture choice -> training (transfer-learn first) -> eval by task (mAP/IoU/CER/OKS) -> serving/edge placement. CV-specific leakage (scene-aware split, augment-after-split). Seams back to training/serving/monitoring agents.
Prevent training-serving skew: compute features once via a feature store or shared transformation so training and serving use identical logic, with point-in-time correctness for temporal features and no leakage of future data.
Playbook for setting up and operating an experiment tracking system (MLflow or Weights and Biases) — what to log, run comparison workflow, promotion to the model registry, and avoiding the common leakage and cherry-picking pitfalls.
Keep production models honest: monitor input/data drift, prediction/concept drift, and performance decay (when labels arrive); define the retraining trigger up front (schedule/threshold/drop); alert on model health; and close the loop to retraining.
Serve models reliably: choose online vs batch by the use case, deploy a versioned model from the registry, optimize latency to a budget (batching/quantization/distillation/hardware), and roll out safely with shadow -> canary -> full.
Build reproducible training: a versioned prep->train->evaluate->register pipeline (not a notebook), experiment tracking (params/metrics/code/data/env), a model registry as source of truth, and leakage-free time-aware validation.
Plugin manifests1
{
"name": "ml-engineering",
"version": "0.4.6",
"description": "ML-engineering (MLOps) team — agents (ml-platform-architect, training-pipeline-engineer, model-serving-engineer, ml-monitoring-engineer) for the PRODUCTION lifecycle of ML models: the platform/architecture (build-vs-buy, the stack, the train->register->serve->monitor loop), reproducible training pipelines + experiment tracking + a model registry, feature stores and train/serve consistency (avoiding training-serving skew and leakage), model serving (online vs batch, shadow/canary), monitoring (data + concept drift, decay, retraining triggers), and computer-vision MLOps (task->architecture + edge-vs-cloud inference). skills, a decision-tree knowledge bank (serving-pattern + retraining + computer-vision trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Seams: significance -> applied-statistics, data pipelines -> data-platform/data-streaming, LLM/agent apps -> claude-app-engineering, deploy -> devops-cicd/cloud-native-kubernetes. Requires ravenclaude-core@>=0.7.0.",
"author": {
"name": "Matt Corbett"
},
"homepage": "https://github.com/mcorbett51090/RavenClaude",
"license": "MIT",
"keywords": [
"mlops",
"machine-learning",
"model-training",
"experiment-tracking",
"feature-store",
"model-registry",
"model-serving",
"drift-detection",
"model-monitoring",
"training-serving-skew",
"mlflow",
"retraining"
],
"requires": {
"plugins": [
"ravenclaude-core@>=0.7.0"
]
},
"lspServers": "./.lsp.json"
}For maintainers
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[ml-engineering on Agent Plugins Marketplace](https://pluginsmp.com/plugins/ml-engineering-2)