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Ml Data Pipeline Architecture

Checked
terrylica·vlatest·data·MIT

Helps with machine learning, data, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add terrylica/cc-skills --skill ml-data-pipeline-architecture -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

terrylica

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Live repository

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

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    Makes no network calls.

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    Scanned terrylica/cc-skills at 3a1dc29965.

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Commit
3a1dc29965
Manifest
plugins/devops-tools/skills/ml-data-pipeline-architecture/SKILL.md
Scanned
2 files, 340 lines
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About this skill

What it does

This skill assists in designing and optimizing machine learning data pipelines, with a strong focus on efficiency and performance. It provides guidance on choosing between Polars and Pandas for data processing, implementing zero-copy architectures using Arrow to minimize memory usage, and integrating ClickHouse databases into PyTorch workflows. The skill leverages patterns and best practices for building robust and scalable ML data pipelines within DevOps and CI/CD environments.

When to use it

  • When deciding between Polars and Pandas for a data pipeline task.
  • To optimize memory usage when working with large datasets using Arrow's zero-copy capabilities.
  • For loading data from ClickHouse databases into PyTorch DataLoaders efficiently.
  • When implementing lazy evaluation techniques to handle very large datasets.
  • If you need to migrate existing Pandas codebases to Polars for improved performance.

Key capabilities

  • Polars vs. Pandas Decision Guidance: Provides a decision tree based on dataset size and operations to help choose the appropriate library.
  • Zero-Copy Architecture Implementation: Demonstrates how to minimize memory copies when moving data between databases, pandas DataFrames, NumPy arrays, and PyTorch tensors using Arrow.
  • ClickHouse Integration Patterns: Offers multiple patterns for integrating ClickHouse databases with Polars and PyTorch, including Arrow streaming and native Polars support.
  • Lazy Evaluation Support: Guidance on utilizing lazy evaluation techniques within Polars for large datasets.

Example prompts

  • "I have a dataset of 50 million rows. Should I use Pandas or Polars?"
  • "How can I load data from ClickHouse into PyTorch without creating unnecessary copies in memory?"
  • "Show me an example of using Arrow to efficiently process data from ClickHouse with Polars."

Tips & gotchas

  • Polars Preference: This skill defaults to using Polars. To use Pandas, you must add # polars-exception: <reason> at the top of the file.
  • Self-Evolving Skill: The skill is designed to improve through usage; fix any incorrect instructions or workarounds directly within this file.
  • Version Requirements: When using Polars' native ClickHouse support, ensure compatibility by checking version requirements (see pola.rs).