# Ml Ops

> Deploy, monitor, and manage ML models in production. Use when setting up model serving, experiment tracking, or ML infrastructure.

- **Type:** Skill
- **Install:** `agentstack add skill-ihatesea69-kiro-kit-ml-ops`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ihatesea69](https://agentstack.voostack.com/s/ihatesea69)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** https://github.com/ihatesea69/kiro-kit/tree/main/.kiro/skills/ml-ops
- **Website:** https://www.npmjs.com/package/kiro-kit

## Install

```sh
agentstack add skill-ihatesea69-kiro-kit-ml-ops
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# MLOps

Activate this skill when deploying models or managing ML infrastructure.

## When to Use

- Deploying models to production endpoints
- Setting up experiment tracking (MLflow, W&B)
- Building model registries and versioning
- Implementing A/B testing for models
- Monitoring model drift and performance

## Core Tools

- **MLflow**: Experiment tracking, model registry
- **Weights & Biases**: Experiment visualization
- **BentoML/Ray Serve**: Model serving
- **DVC**: Data and model versioning
- **Evidently AI**: Model monitoring

## Patterns

```python
import mlflow

mlflow.set_experiment("classification_v2")
with mlflow.start_run():
    mlflow.log_params({"lr": 0.001, "epochs": 50})
    mlflow.log_metrics({"accuracy": 0.94, "f1": 0.91})
    mlflow.sklearn.log_model(model, "model")
```

## Rules

- Version everything: code, data, models, configs
- Automate training pipelines (no manual steps)
- Monitor prediction distributions for drift
- Implement rollback mechanisms for model updates
- Log all experiments, even failed ones

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** [ihatesea69/kiro-kit](https://github.com/ihatesea69/kiro-kit)
- **License:** MIT
- **Homepage:** https://www.npmjs.com/package/kiro-kit

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ihatesea69-kiro-kit-ml-ops
- Seller: https://agentstack.voostack.com/s/ihatesea69
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.