ML Workflow

What problem does it solve?

This Skill streamlines the complex process of designing and implementing machine learning workflows, from initial problem definition to ongoing monitoring.

Core Features & Use Cases

• End-to-End Design: Covers feature engineering, experiment tracking, training pipelines, model serving, A/B testing, and drift monitoring.
• Tool Agnostic: Provides a framework for selecting and integrating various ML tools and platforms.
• Use Case: A data science team needs to build a new recommendation engine. This Skill helps them define the problem, select features, choose an experiment tracking tool like MLflow, design the training and serving infrastructure, and set up monitoring for performance degradation.

Quick Start

Design an ML workflow for a classification problem using the ML Workflow skill.