ml-pipeline-automation
Build production ML pipelines that handle data ingestion, model training, and deployment with Airflow DAGs, Kubeflow containers, and MLflow experiment tracking. This skill covers orchestration patterns, scheduling retraining jobs, managing model versions, and troubleshooting common workflow failures.
ML Pipeline Automation orchestrates end-to-end machine learning workflows using Airflow, Kubeflow, and MLflow.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-25
ML Pipeline Automation orchestrates end-to-end machine learning workflows using Airflow, Kubeflow, and MLflow. Build production ML pipelines that handle data ingestion, model training, and deployment with Airflow DAGs, Kubeflow containers, and MLflow experiment tracking. This skill covers orchestration patterns, scheduling retraining jobs, managing model versions, and troubleshooting common workflow failures.
Use it when
- ml-pipeline-automation covers both orchestration approaches.
- ml-pipeline-automation integrates MLflow for comprehensive experiment tracking and model versioning.
Verify before relying
Read SKILL.md below before installing (4 files). Open directory: indexed for reading, not audited.
Install
secondsky/claude-skills/ml-pipeline-automation · repository language: TypeScript
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to automate machine learning workflows with ml-pipeline-automation?
ml-pipeline-automation enables you to build production ML pipelines using Airflow DAGs and Kubeflow containers for orchestrating data ingestion, model training, and deployment. You define workflow dependencies, schedule automated retraining jobs, and monitor pipeline execution end-to-end. The skill covers orchestration patterns that handle complex task dependencies and recovery from failures.
What's the difference between Kubeflow vs Airflow for ML?
ml-pipeline-automation covers both orchestration approaches. Airflow excels at scheduling and DAG-based workflows with broad integration support, while Kubeflow specializes in containerized ML workloads with native Kubernetes integration. The skill teaches when to use each: Airflow for general data pipelines and Kubeflow for distributed training and complex ML-specific orchestration needs.
How does ml-pipeline-automation handle model versioning with MLflow?
ml-pipeline-automation integrates MLflow for comprehensive experiment tracking and model versioning. You log metrics, parameters, and artifacts during training runs, compare experiments, and manage model versions in a centralized registry. This enables reproducible pipelines where you can track which data, code, and hyperparameters produced each model version.
Can ml-pipeline-automation automate model retraining schedules?
Yes. ml-pipeline-automation teaches you to schedule reproducible model retraining pipelines using Airflow's scheduling capabilities. You can set up automated triggers based on time intervals or data drift detection, manage dependencies between retraining and validation steps, and implement monitoring to alert on pipeline failures or performance degradation.
What MLOps infrastructure does ml-pipeline-automation implement?
ml-pipeline-automation covers MLOps infrastructure including pipeline orchestration, experiment tracking with MLflow, model artifact management, and monitoring with alerts. The skill addresses reproducible pipeline setup, task dependency handling, data validation sensors, and troubleshooting common workflow failures in production environments.
How does ml-pipeline-automation help with drift detection?
ml-pipeline-automation integrates monitoring capabilities to detect model and data drift in production. You can implement validation checks within your orchestrated pipelines, set up alerts when drift is detected, and trigger automated retraining workflows. This ensures your models remain accurate and your pipelines respond automatically to changing data distributions.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
ML Pipeline Automation
Orchestrate end-to-end machine learning workflows from data ingestion to production deployment with production-tested Airflow, Kubeflow, and MLflow patterns.
When to Use This Skill
Load this skill when: - Building ML Pipelines: Orchestrating data → train → deploy workflows - Scheduling Retraining: Setting up automated model retraining schedules - Experiment Tracking: Tracking experiments, parameters, metrics across runs - MLOps Implementation: Building reproducible, monitored ML infrastructure - **Workflow
(truncated - see the full file via the links below)
File tree — 4 files
plugins/ml-pipeline-automation/skills/ml-pipeline-automation/SKILL.md
plugins/ml-pipeline-automation/skills/ml-pipeline-automation/references/airflow-patterns.md
plugins/ml-pipeline-automation/skills/ml-pipeline-automation/references/kubeflow-mlflow.md
plugins/ml-pipeline-automation/skills/ml-pipeline-automation/references/pipeline-monitoring.md
Let your AI agent find skills like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 56,283 agent skills by what they can do, searchable in plain language.
wish › “Automate end-to-end ML workflows with orchestration tools”
Give your agent the search over MCP, or paste the wish link into any chat. No install? Search from any chat →
Related skills
ML Pipeline Automation handles the complete machine learning lifecycle, automating data collection, preprocessing, model training, validation, and production deployment. Built on orchestration platforms like Airflow and Kubeflow, it ensures reproducible and scalable workflows with integrated monitoring and performance tracking.
ML MLOps guides you through building auditable, repeatable machine learning workflows. It covers experiment tracking, model versioning and governance, pipeline orchestration across Kubernetes, AWS, GCP, and Azure, CI/CD automation, and production monitoring to catch drift and quality issues.
This skill generates Apache Airflow DAGs tailored for construction data workflows, handling everything from BIM model processing to validation and cost reporting. It provides a builder interface for composing tasks—bash commands, Python functions, file sensors, and branching logic—then outputs production-ready DAG code. Pre-built templates accelerate common patterns like BIM validation pipelines.
airflow-expert provides deep knowledge for building reliable, scalable data pipelines with Airflow. Learn DAG fundamentals, task dependencies, branching logic, dynamic task generation, and production operations patterns to orchestrate complex workflows.
ML-Strategy generates trading signals by applying sklearn classifiers (RandomForest, GradientBoosting, Ridge) to engineered OHLCV features with walk-forward validation to prevent data leakage. The skill extracts momentum, volatility, and technical indicators, trains models incrementally, and outputs clean directional signals in the [-1.0, 1.0] range.
Learn to architect and deploy data pipelines with Apache Airflow, covering DAG design, operators, task dependencies, branching logic, and scheduling patterns. This skill guides you through building scalable workflows from basic structures to advanced patterns like dynamic task generation and conditional execution. Deploy production-grade orchestration across single machines or distributed clusters.
More skills airflow-dag-patterns (MIT) · Mlops Workflows (unlicensed) · ml-pipeline-workflow (MIT)