End-to-end machine learning projects involve the complete process of developing a machine learning model, starting from data collection and preprocessing to training, evaluation, and deployment. These projects encompass data exploration, feature engineering, model selection, performance evaluation, and integration with production systems
machine-learning-model-deployment data-collection-and-preprocessing model-training-and-tuning feature-engineering-techniques performance-evaluation-metrics real-time-prediction-systems automation-in-ml-pipelines model-monitoring-and-maintenance
-
Updated
Feb 25, 2025 - Jupyter Notebook