Ml Pipeline
Designs production-grade ML pipeline infrastructure — experiment tracking, orchestrated training DAGs, feature stores, model registries, automated retraining and validation.
Before & after
Same prompt, same agent — the only difference is the skill.
Moving from manual retraining to a production ML pipeline is a big step forward. Here's what a production-grade pipeline typically includes: - **Experiment tracking** (MLflow, Weights & Biases) to log runs - **Orchestration** (Airflow, Kubeflow) to schedule and manage workflows - **Feature store** (Feast) for consistent features across training and serving - **Model registry** to version and stage models - **Automated retraining and validation** with quality gates Start by adding experiment tracking so you have visibility, then move to orchestration for automation. What does your current setup look like?
"Breaks constantly" usually means one of three specific failures, and
the fix order matters — automate on a broken foundation and you've
automated the breakage.
FIRST, WHICH BREAK IS IT?
Train/serve skew (features differ between the two) → feature store
Silent quality regression (bad model ships) → validation gate
Manual step forgotten → orchestration
Most "constantly breaking" pipelines are the first. Diagnose before
building.
BUILD ORDER — deliberately not alphabetical
1. EXPERIMENT TRACKING (MLflow / W&B) — do this first, always
You can't fix what you can't see. Log every run's params, metrics,
data version. This is one afternoon and it makes every later step
debuggable.
2. FEATURE STORE (Feast) — if it's train/serve skew
One definition of each feature, used by both training and serving.
This is what kills "works in training, wrong in prod." Schema the
features here, not in two places.
3. MODEL REGISTRY + VALIDATION GATE
A model is promoted staging→prod ONLY if it beats the current one
on held-out data. This gate is what stops a bad retrain from
shipping — the "breaks constantly" you described is often a bad
model auto-deploying with no gate.
4. ORCHESTRATION (Airflow / Kubeflow DAG)
NOW automate — retrain → validate → register → deploy, with the
gate wired in. Automating before step 3 just ships bad models
faster.
5. DATA VERSIONING (DVC)
So a retrain is reproducible: same data version + same code = same
model. Without it, "it worked last week" is unfalsifiable.
THE PRINCIPLE
Visibility → correctness → automation. In that order. The pipeline
that breaks constantly almost always automated before it had the
validation gate.
What's the actual failure — wrong predictions in prod, or a step that
falls over? That picks whether we start with Feast or the gate.About this skill
name: ml-pipeline description: Use when Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, buil...
Ml Pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.
What you get
- Public GitHub repo
- the skills/ml-pipeline folder with SKILL.md and references.
Customize your output
- Fork the repo and extend the skill's reference files for your own stack conventions.
Example output
Activates on a matching request (e.g. building or reviewing Ml Pipeline code) and can chain with other skills in the pack.
Best for
Full-stack developers and engineering teams using Claude Code.
SKILL.md preview
---
name: ml-pipeline
description: Use this skill when building or orchestrating ML pipeline infrastructure, including experiment tracking, training DAGs, feature stores, and automated retraining.
version: 1.0.0
category: Development / Data & ML
author: AgentVolt
license: proprietary
tags:
- development
- data-ml
---
# Ml Pipeline
Designs production-grade ML pipeline infrastructure — experiment tracking, orchestrated training DAGs, feature stores, model registries, automated retraining and validation.
## When to use
… (sign up to view the full skill)More development skills
View all Development skills →Pandas Pro
Performs pandas DataFrame operations for data analysis, manipulation, and transformation.
Spark Engineer
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads.
Fine Tuning Expert
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks.
RAG Architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines.