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MLOps

Deploy and maintain machine learning models in production with drift detection and retraining.

1|Updated Apr 1, 2026
Search Tags:#monitoring#CI/CD#MLOps#model deployment#retraining
One-click install
npx skills add https://github.com/2nth-ai/skills --skill mlops-2nth-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: MLOps
Source: https://github.com/2nth-ai/skills/tree/main/data/ml-ops
Command: npx skills add https://github.com/2nth-ai/skills --skill mlops-2nth-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, seldon-core, kserve, vertex-ai, sagemaker, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps in deploying, monitoring, and improving machine learning models in production, bridging the gap from model training to reliable model operation.

Core Features & Use Cases

  • Model Serving: Provides various model serving patterns like batch scoring, real-time API, and async/event-driven scoring.
  • Feature Stores: Solves feature consistency problems and improves model performance.
  • Model Registry and Versioning: Ensures every model artifact is versioned and tracked with essential metadata.
  • Monitoring and Drift Detection: Monitors models for drift and provides retraining triggers.
  • Retraining Pipelines: Automates retraining processes based on drift, performance, or scheduled intervals.
  • CI/CD for ML: Integrates machine learning pipelines with CI/CD processes for efficient model deployment and management.

Quick Start

Use the MLOps skill to monitor model performance and retrain when necessary, ensuring your machine learning models stay accurate and effective.

Frequently Asked Questions about MLOps

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

FAQPage Schema
How do I automate model retraining when data drift occurs in production?▼

Automate model retraining by monitoring for data drift and configuring triggers that initiate retraining pipelines. This ensures machine learning models maintain accuracy when production data distributions change over time.

What is the best way to deploy machine learning models using CI/CD pipelines?▼

The best way to deploy models is integrating ML pipelines with CI/CD processes. This approach manages model registry versioning and supports serving patterns like real-time APIs and batch scoring.

Does this MLOps approach work with SageMaker and Vertex AI?▼

Yes, this MLOps approach works with SageMaker and Vertex AI. It leverages these platforms alongside tools like MLflow and KServe for comprehensive end-to-end model management and deployment.

How do I maintain feature consistency between training and serving?▼

Maintain feature consistency by utilizing feature stores. This solves discrepancies between training and serving environments, directly improving model performance and reliability in production.

What are the different model serving patterns for production deployment?▼

Model serving patterns include batch scoring, real-time API, and asynchronous event-driven scoring. These options allow flexible deployment based on specific latency and throughput requirements.

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