claude

machine-learning-ops

ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows

0 on GitHubby Seth Hobsonv1.2.1MITupdated 1 month agoai-ml
/

Plugin machine-learning-ops from marketplace claude-code-workflows in authrain-cloud-abdullahformuli/claude-game-dev. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (1)

  • ml-pipeline-workflow

    Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

Slash commands (1)

  • /machine-learning-ops:ml-pipeline

Subagents (3)

  • data-scientist

    Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.

  • ml-engineer

    Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

  • mlops-engineer

    Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

View source on GitHub

More from authrain-cloud-abdullahformuli/claude-game-dev

All 66 plugins →

Backend API design, GraphQL architecture, workflow orchestration with Temporal, and test-driven backend development

5 skills · 1 command · 4 agents

0

CI/CD pipeline configuration, GitHub Actions/GitLab CI workflow setup, and automated deployment pipeline orchestration

4 skills · 1 command · 5 agents

0

Cloud architecture design for AWS/Azure/GCP, Kubernetes cluster configuration, Terraform infrastructure-as-code, hybrid cloud networking, and multi-cloud cost optimization

4 skills · 6 agents

0

Metrics collection, logging infrastructure, distributed tracing, SLO implementation, and monitoring dashboards

4 skills · 2 commands · 4 agents

0

Framework updates, migration planning, and architectural transformation workflows

4 skills · 3 commands · 2 agents

0

LLM application development, prompt engineering, and AI assistant optimization

4 skills · 3 commands · 2 agents

0

Similar plugins

Query Trackio projects, runs, and metrics using the `trackio` CLI. Use when the user needs to list projects/runs/metrics, get summaries, or retrieve metric values from local Trackio databases. Supports both human-readable and JSON output formats. Covers project discovery, run inspection, metric querying, and system metrics. Designed for LLM agents and automation scripts.

1 skill

sickn33/agentic-awesome-skills 49,620

Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams

26 skills · 4 commands · 3 agents · 1 MCP server

anthropics/claude-plugins-official 47,076

llm-finetuning

claudecodex

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

wshobson/agents 42,461

Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.

1 skill · 3 commands · 1 agent

alirezarezvani/claude-skills 29,725

Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture

VoltAgent/awesome-claude-code-subagents 26,513

Agents for data engineering, machine learning, and AI development

12 agents

davepoon/buildwithclaude 3,741

Maintain machine-learning-ops?

This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.

claude & codex plugins66 plugins