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Engineering Dec 10, 2024 6 min read
Building Scalable ML Pipelines with Python
Best practices for designing production-ready machine learning infrastructure.
Moving a model from a Jupyter notebook to a production environment is a significant challenge. Scalable ML pipelines are the backbone of reliable AI applications.
Modular Codebase
Break down your pipeline into reusable components: Data Ingestion, Preprocessing, Training, and Evaluation. This ensures maintainability and easier testing.
Containerization
Docker is your friend. Containerizing your ML environment ensures consistency across development, testing, and production, eliminating "it works on my machine" issues.
Orchestration
Tools like Airflow or Kubeflow are essential for managing dependencies and scheduling workflow execution, especially when dealing with large datasets and complex retraining cycles.