mlops-dag-builder

What This Skill Does

The mlops-dag-builder skill is a specialized architectural engine designed for constructing robust, production-grade DAG-based MLOps pipelines. It supports major orchestration frameworks including Apache Airflow, Dagster, Kubeflow, and Prefect. Instead of focusing on simple script execution, this skill operates at the systems architecture level, helping you define component dependencies, data flow logic, error handling strategies, and retry policies for complex, multi-stage machine learning workflows. It empowers users to transition from local notebook experimentation to scalable, platform-agnostic infrastructure.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/anton-abyzov/sw-mlops-dag-builder

Use Cases

This skill is ideal for building scalable data pipelines that include ingestion, validation, feature engineering, distributed training, and automated model deployment. It is particularly useful for platform engineers who need to design reusable CI/CD templates for ML projects, ensuring consistent environments across cloud services like AWS SageMaker, Google Vertex AI, or Azure ML. It serves as the bridge between raw code and automated production delivery.

Example Prompts

1. "Build a DAG in Dagster that orchestrates a daily feature engineering job, trains a PyTorch model on the new data, and pushes the artifact to an S3 bucket only if performance metrics improve."
2. "I need a Kubeflow pipeline design that includes data validation steps using Great Expectations followed by a distributed training job with fault-tolerant retry logic."
3. "Compare airflow and prefect for managing high-frequency inference workflows; provide a boilerplate implementation for a blue-green deployment strategy using this skill."

Tips & Limitations

When designing your pipelines, always prioritize idempotency in your task definitions to ensure that retries do not corrupt stateful data. Ensure that you have a clearly defined data versioning strategy before implementing the training phase, as pipeline reliability is heavily dependent on data lineage. Note that this skill is strictly for DAG orchestration architecture and platform-agnostic workflows. If you are working specifically with SpecWeave increment-based ML experiments or require tight experiment tracking hooks, please utilize the 'ml-pipeline-orchestrator' instead to avoid configuration conflicts. Always keep your pipeline code decoupled from the orchestration logic to ensure easier testing and porting between different providers.