Ml Pipeline

ml-pipeline enables ML engineers and data scientists to design and implement production-grade machine learning infrastructure by configuring experiment tracking systems (MLflow, Weights & Biases), orchestrating training workflows with tools like Kubeflow and Airflow, building feature stores using Feast, and automating model validation and retraining processes. It solves the complexity of managing end-to-end ML operations by providing structured guidance for data validation, distributed training, hyperparameter tuning, and model deployment workflows. The skill is specifically designed for teams building scalable, reproducible ML pipelines that require robust orchestration, experiment tracking, and model lifecycle management.