moai-domain-ml-ops

Name: moai-domain-ml-ops
Availability: InStock
Author: ajbcoding

Orchestrate enterprise ML lifecycle workflows across Kubernetes-based MLOps tools.

4|1|Updated Nov 18, 2025

Search Tags:#mlops #mlflow #dvc #kubeflow #ray-serve

Ready to run

npx skills add https://github.com/AJBcoding/claude-skill-eval --skill moai-domain-ml-ops

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: moai-domain-ml-ops
Source: https://github.com/AJBcoding/claude-skill-eval/tree/main/skills/moai-domain-ml-ops
Command: npx skills add https://github.com/AJBcoding/claude-skill-eval --skill moai-domain-ml-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

End-to-end MLOps platform with experiment tracking, data versioning, and model deployment patterns.

Core Features & Use Cases

✅ MLflow for experiment tracking
✅ DVC for data versioning
✅ Kubeflow and Argo for workflows
✅ Model serving with Ray Serve/Seldon Core

Quick Start

Set up an MLflow tracking server and a simple Kubeflow pipeline.

Frequently Asked Questions about moai-domain-ml-ops

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up end-to-end MLOps with experiment tracking and model deployment?▼

End-to-end MLOps orchestrates the full ML lifecycle using MLflow for experiment tracking, DVC for data versioning, Kubeflow for workflows, and Ray Serve or Seldon Core for model serving. This Skill integrates these tools into a cohesive Kubernetes-based platform that manages experiments, data, pipelines, and deployments together.

Can I use MLflow, DVC, and Kubeflow together in a single platform?▼

Yes. This Skill unifies MLflow, DVC, Kubeflow, and complementary tools like Feast for feature stores, Optuna for hyperparameter optimization, and Evidently AI for monitoring. It handles integration across all stages—experiment tracking, data versioning, orchestration, and serving—within Kubernetes deployments.

What's required before deploying ML models with this MLOps setup?▼

Before deployment, you need experiment tracking configured in MLflow, versioned datasets in DVC, orchestrated workflows in Kubeflow or Argo, and hyperparameter optimization through Optuna. This Skill assumes these prerequisites are in place and coordinates their outputs into a deployable model-serving pipeline.

How do I monitor and observe production ML models across my platform?▼

Production monitoring uses Prometheus for metrics collection and Grafana for visualization, integrated with Evidently AI for model performance tracking. This Skill combines observability from artifact storage, model serving logs, and inference metrics into unified dashboards.

Does this support automated CI/CD pipelines for machine learning workflows?▼

Yes. This Skill provides automated CI/CD within Kubernetes by orchestrating MLflow, DVC, Kubeflow, and Argo workflows. It handles continuous experiment tracking, data versioning, pipeline execution, and model deployment as integrated, repeatable stages.

What's the difference between using Ray Serve versus Seldon Core for model serving?▼

Both Ray Serve and Seldon Core are supported for model serving in this platform. Ray Serve scales within Ray clusters; Seldon Core deploys natively on Kubernetes. This Skill accommodates either, depending on your cluster architecture and scaling requirements.