machine-learning-ops
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Paquete de plugins · de Seth Hobson
Resumen
machine-learning-ops es un paquete de plugins para herramientas de programación con IA publicado por Seth Hobson. Marketplaces que leemos y que lo publican: 1 (Community GitHub marketplaces). El último cambio que registramos fue el 2026-11-14. Según nuestra pasada por los marketplaces del 2026-11-15.
Qué pide
- Se instala como paquete de plugins
Ninguno de los marketplaces que leemos publica sus herramientas ni los ajustes que pide.
Lo que declara, pide y ejecuta, completo
Instala solo plugins de desarrolladores en los que confíes. Mostramos lo que publican los marketplaces y no probamos plugins.
Funciona con
- Claude Code
Según los marketplaces que lo listan y el transporte MCP que declara, tal como lo documenta cada cliente. No probamos plugins.
Conectar
Comandos y configuraciones generados solo a partir de lo que declaran sus marketplaces. No los ejecutamos por ti ni probamos plugins.
Como plugin de Claude Code
Generado a partir de lo que declara Community GitHub marketplaces.
/plugin marketplace add wshobson/agents
/plugin install machine-learning-ops@claude-code-workflows
Dónde está publicado
| Marketplace | Estado | Versión | Cifras | Verificado | Visto por primera vez |
|---|---|---|---|---|---|
| Marketplace de la comunidad wshobson/agents (Abrir el código en GitHub) | Publicado | 1.2.3 | Forks (GitHub): 4 491 Estrellas (GitHub): 42 505 |
Sin marca |
Cada cifra es del propio marketplace y nunca se suma entre marketplaces. Un marketplace que no publica cifras no muestra ninguna: sin número no es cero.
Figura en un marketplace de la comunidad
| Marketplace | Repositorio | Estrellas (GitHub) |
|---|---|---|
| claude-code-workflows | wshobson/agents | 42 505 |
Cómo han cambiado sus cifras
- 2026-11-134,5 mil
- 2026-11-144,5 mil
- 2026-11-1342,5 mil
- 2026-11-1442,5 mil
Cada punto es un día en que leímos un número distinto; los tramos planos son días en que el número no cambió. Las cifras de distintos marketplaces nunca se suman.
Qué contiene el paquete
Tal como se publica en Community GitHub marketplaces.
Skills: 2
ml-pipeline-workflowrecsys-pipeline-architect
Comandos: 1
ml-pipeline
Agentes: 3
data-scientistml-engineermlops-engineer
Qué cambió
- Pasó a ser un producto que seguimos
- Community GitHub marketplaces: apareció
Forks y copias
Plugins similares
Productos cuyos propios nombres, descripciones, herramientas y skills son los más cercanos a este por su significado, en todos los marketplaces que leemos. La cercanía se ordena solo dentro de esta lista.
-
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
-
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
-
do-machine-learning-engineering
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
-
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
-
machine-learning
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
-
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment…
-
Model Training Engineer — Model training — algorithm selection, hyperparameter tuning, training infrastructure
-
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
-
ML/AI engineer — model training, MLOps, feature engineering, LLM integration
Plugins similares por marketplace
La misma pregunta a cada marketplace por separado: ¿hay allí un equivalente de este plugin?
Publicado en Claude Code: claude-plugins-community (5)
-
DataRobot Agent Skills - Agentic skills for DataRobot enterprise AI and ML workflows. Covers model training, deployment, predictions, feature engineering, monitoring…
-
20 enterprise-grade skills for autonomous AI/ML research lab operations. Includes 10 custom gap-analysis skills (research-commander, agent-debugger, model-evaluator…
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
Claude model selection advisor — recommends optimal model for each task via shell hook. Zero attack surface.
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
Publicado en Claude Code: claude-plugins-official (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
Build, train, evaluate, and use open source AI models, datasets, and spaces.
Publicado en Claude: knowledge-work-plugins (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
AI-first project auditor and re-engineer based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
A suite of agent skills for the full GrowthBook feature flag and experimentation lifecycle.
Publicado en Codex: openai-curated (openai/plugins) (5)
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
Build with OpenAI APIs, Agents SDK, and ChatGPT Apps, and create and save OpenAI API keys from Codex.
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
Answer product and business questions with data
-
CrowdStrike Falcon Foundry development skills for building cybersecurity applications on the Falcon platform. Includes UI development, collections, functions, workflows, API…
Publicado en Community GitHub marketplaces (5)
-
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
-
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
-
do-machine-learning-engineering
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
-
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
-
machine-learning
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
Publicado en Copilot CLI: github/awesome-copilot (5)
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Skills and interactive Copilot canvas for designing, configuring, testing and deploying agents to Microsoft Foundry.
-
Phoenix AI observability skills for LLM application debugging, evaluation, and tracing. Includes CLI debugging tools, LLM evaluation workflows, and OpenInference tracing…
-
Arize AX platform skills for LLM observability, evaluation, and optimization. Includes trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations…
Publicado en Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
model-apps
Build and deploy Power Apps generative pages for model-driven apps with specialist agents for planning, entity creation, and parallel code generation. Requires PAC CLI >= 2.7.0…
-
Complete bundle: all Microsoft Skills for Fabric for developers and consumers.
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
-
Bring Azure DevOps work-item, repository, and pull-request workflows into GitHub Copilot.
Publicado en Cursor: cursor/plugins (5)
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
Ask Databricks Genie questions about your workspace's data in plain language.
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
if you want to go fast, go deep first. pstack helps you write less, but higher quality code. rigorous agent workflows you can parallelize with confidence.
-
Consult a stronger model before major decisions, when stuck, and before declaring done.
Publicado en Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
Analyze documents, images, videos, and audio.
-
Seamless interaction with Novita AI platform resources.
-
Wayfound’s MCP server allows business users to govern, supervise, and improve AI Agents.
Publicado en Official MCP Registry (5)
-
Train AutoML models on CSV files and run predictions from Claude and other MCP clients.
-
Discover, shortlist, compare, cost-model, and live-test 300+ AI models from your editor
-
Search, rank and compare open-source AI projects with Archestack's open data
-
Find AI models that fit a job from your requirements, sourced benchmarks and real cost.
-
Give your AI a research team. Forecast, score, classify, or research every row of a dataset.
Publicado en PyPI (MCP servers) (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Hanzo MCP Server - Unified tool surface with AI, cloud control, vector search, and multi-framework UI
-
lineai-mcp-server io.github.lineai-intelligence/lineai-mcp-server
An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
-
MCP server for Empirica — AI measurement and calibration tools via Model Context Protocol
-
air-blackbox-mcp io.github.airblackbox/air-blackbox-mcp
EU AI Act compliance scanner for Python AI agents. 14 tools across 6 articles.
Publicado en npm registry (MCP servers) (5)
-
mcp-server io.github.metrxbots/mcp-server
Track AI agent costs, detect waste, optimize models, and prove ROI. 23 MCP tools across 10 domains.
-
where-my-hug io.github.bikemeardsley/where-my-hug
Which parts of an ML need Hugging Face already covers. Every model and dataset checked live.
-
Intelligent CLI tool with AI-powered model selection that analyzes your hardware and recommends optimal LLM models for your system
-
Contextual prompts and agent skills for 140+ AI platforms.
-
Hanzo MCP Server - Unified tool surface with AI, cloud control, vector search, and multi-framework UI
Encontrados por significado, no por código ni repositorio compartidos: los plugins similares siguen siendo productos distintos, y esto no es una reseña ni una recomendación.
¿Quieres esta marca para tu plugin en nuestro catálogo?
Demuestra que lo mantienes con un archivo en su repositorio, un archivo en tu dominio o un registro DNS, y podrás mostrar esta marca en la ficha, con banners en vivo para tu web y tu README. Cómo funciona la verificación
Además recibirás avisos cuando un marketplace lo publique, lo retire, cambie su marca verificada o lo que pide, y su endpoint remoto quedará monitorizado como un servicio web.
Datos de Community GitHub marketplaces. Las cifras proceden de nuestros propios recorridos periódicos por los marketplaces, leída por última vez el . No publicamos datos de contacto de los editores de plugins.