sota-ml-engineering

What problem does it solve?

This Skill provides a comprehensive set of rules and guidelines for building and auditing machine learning systems, ensuring they are robust, secure, and ready for production.

Core Features & Use Cases

• ML System Architecture: Defines best practices for designing ML systems with a focus on the model as part of a larger system.
• Data and Features: Offers guidance on data and feature management, including data leakage, train/serve skew, and versioning.
• Training and Experimentation: Provides reproducibility and tracking rules for training and experimentation processes.
• Evaluation and Validation: Ensures models are evaluated against business objectives and are production-ready.
• Deployment and Serving: Covers model packaging, serving patterns, and operational considerations.
• Monitoring and Drift: Focuses on ongoing monitoring of model performance and data drift.
• Security and Governance: Offers guidelines for security best practices and compliance with regulatory standards.
• Use Case: Utilize this Skill to build a machine learning system that is reproducible, secure, and compliant with industry standards.

Quick Start

Run the 'build-ml-system' command to initiate the process of building an ML system according to the sota-ml-engineering rules.