Machine Learning Models Without Pipelines
This refers to the practice of developing and deploying machine learning models without using automated pipeline frameworks like MLflow, Kubeflow, or Apache Airflow. It involves manually handling data preprocessing, model training, evaluation, and deployment as separate, often ad-hoc, steps. This approach is common in prototyping, small-scale projects, or when learning ML fundamentals, but it can lead to challenges in reproducibility, scalability, and maintenance.
Developers should learn this approach when starting with machine learning to understand core concepts like data cleaning, feature engineering, and model evaluation without the overhead of pipeline tools. It's useful for quick experiments, academic projects, or when working with simple datasets where automation isn't necessary. However, for production systems, it's generally recommended to transition to pipeline-based workflows to ensure efficiency and reliability.