Custom ML Pipelines

Custom ML Pipelines refer to the design and implementation of tailored, end-to-end workflows for machine learning projects, encompassing data ingestion, preprocessing, model training, evaluation, and deployment. They involve creating modular, reproducible, and scalable sequences of steps specific to a project's requirements, often using tools like Apache Airflow, Kubeflow, or custom scripts. This approach ensures consistency, automation, and efficiency in ML operations, enabling teams to manage complex data science workflows effectively.

Also known as: Machine Learning Pipelines, ML Workflows, Data Science Pipelines, MLOps Pipelines, Custom ML Workflows

Why learn Custom ML Pipelines?

Developers should learn and use custom ML pipelines when working on production-grade machine learning systems that require automation, reproducibility, and scalability, such as in industries like finance, healthcare, or e-commerce. They are essential for handling large datasets, frequent model retraining, and deployment in cloud or on-premise environments, as they reduce manual errors and streamline the ML lifecycle from data to insights.

Learning Resources

• docs: MLOps: Machine Learning Operations
• tutorial: Building Machine Learning Pipelines with TensorFlow Extended
• docs: Apache Airflow Documentation
• tutorial: Kubeflow Pipelines Tutorial
• course: Coursera: Machine Learning Engineering for Production (MLOps)