ML Pipelines

What problem does it solve?

This Skill automates the complex, multi-step process of building, training, evaluating, and deploying machine learning models, ensuring reproducibility and efficiency.

Core Features & Use Cases

• End-to-End Orchestration: Manages the entire ML lifecycle from data ingestion to model deployment.
• Reproducibility & Versioning: Ensures consistent results and tracks changes in data, code, and models.
• Scalability: Supports distributed execution for large-scale ML tasks.
• Use Case: Deploying a new fraud detection model by automating data preprocessing, model training with hyperparameter tuning, evaluation against a validation set, and finally deploying the best performing model to a production API.

Quick Start

Use the ML Pipelines skill to define and compile a Kubeflow pipeline for training a model using the provided data path.