The machine learning (ML) lifecycle refers to the comprehensive process of developing and deploying ML models to address real-world challenges. It generally encompasses a sequence of key steps: data preparation, model training and evaluation, model deployment, and model monitoring and maintenance.
The initial stage of the ML lifecycle is data preparation. This involves gathering and preprocessing the data utilized for training and evaluating the model. Specific tasks may include cleaning and formatting the data, selecting pertinent features, and partitioning the data into training and test datasets.
Subsequently, the model is trained with the prepared data, followed by an evaluation of its performance using a set of metrics. This phase often requires techniques like hyperparameter tuning to optimize crucial hyperparameters, such as the learning rate or regularization coefficient.
Once trained and evaluated, the model can generate predictions or make decisions based on new, unseen data, paving the way for its deployment in a production environment. Deployment might involve creating a new standalone application or integrating the model into an existing one.
The final step in the ML lifecycle is model monitoring and maintenance. This entails continuously tracking the model's performance over time and making timely updates or adjustments to ensure its sustained effectiveness. Such actions may include retraining the model with new data or fine-tuning its hyperparameters as circumstances change.