skills.rest
devendrapratapsingh
devendrapratapsinghCOMMUNITY
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mlops

Automate ML model deployment, training pipelines, and monitoring with Kubeflow, MLflow, Feast, and Seldon Core.

1|Updated Mar 11, 2026
Search Tags:#mlops#machine learning#model deployment#feature store#model monitoring#ml infrastructure#training pipelines
One-click install
npx skills add https://github.com/devendrapratapsingh/bizbuddy-ai-agent --skill mlops-devendrapratapsingh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlops
Source: https://github.com/devendrapratapsingh/bizbuddy-ai-agent/tree/main/.claude/skills/mlops
Command: npx skills add https://github.com/devendrapratapsingh/bizbuddy-ai-agent --skill mlops-devendrapratapsingh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill streamlines the entire lifecycle of machine learning models, from development to production deployment and ongoing monitoring, ensuring models are reliable, scalable, and performant.

Core Features & Use Cases

  • Model Deployment: Containerize and deploy models using various serving patterns (REST, gRPC, batch).
  • Training Pipelines: Orchestrate complex training workflows with data versioning and experiment tracking.
  • Monitoring: Detect data drift, model performance degradation, and set up alerts.
  • Use Case: Deploy a real-time fraud detection model, set up continuous monitoring for data drift, and automatically retrain the model when performance degrades.

Quick Start

Use the mlops skill to deploy the latest version of the churn prediction model to a Kubernetes cluster.

Frequently Asked Questions about mlops

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

FAQPage Schema
How do I automate machine learning model deployment and training pipelines?▼

Automate machine learning model deployment and training pipelines by orchestrating complex workflows with data versioning and experiment tracking using tools like MLflow and Kubeflow. This Skill handles end-to-end MLOps lifecycle automation.

What is the best way to monitor machine learning models for data drift in production?▼

The best way to monitor machine learning models for data drift is by detecting performance degradation and setting up automated alerts. This Skill enables ongoing monitoring to ensure model reliability and can trigger automated retraining.

Can I deploy models to Kubernetes using REST and gRPC serving patterns?▼

Yes, you can deploy models to Kubernetes using REST, gRPC, and batch serving patterns. This Skill leverages Seldon Core to containerize and deploy models, addressing challenges in productionizing ML models at scale.

How does a feature store integrate with ML infrastructure for continuous training?▼

A feature store integrates with ML infrastructure by standardizing feature serving for both training and inference. This Skill leverages Feast alongside Kubeflow to orchestrate complex training workflows and manage ML infrastructure.

When do I need to set up automated retraining for real-time fraud detection models?▼

You need to set up automated retraining for real-time fraud detection models when ongoing monitoring detects data drift or performance degradation. This Skill automates retraining to ensure model reliability under changing data conditions.

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