Building ai Systems with Code
Develop scalable, ethical, and production-ready machine learning solutions using modern engineering practices
Core Principles
Learn about best practices for clean, maintainable, and production-ready code in machine learning projects
Begin Coding →Toolchain
Discover modern tooling for model versioning, pipeline orchestration, and continuous integration/continuous delivery
See Tools →Core Development Practices
Modular Design
Architect code with separation of concerns for data processing, model logic, and evaluation metrics
Reproducibility
Use version control, random seed management, and environment containers for consistent results
Testing
Implement unit tests for feature pipelines, model validation, and production monitoring
Engineering Toolchain
MLflow
Model versioning and artifact storage for end-to-end machine learning pipelines
DVC
Dataset versioning and pipeline management for reproducible experiments
Airflow
Orchestration for complex data pipelines and model deployments