Process configuration in Machine Learning involves setting up workflows and parameters to streamline data handling, model training, and evaluation.
- Gather relevant data from various sources - Ensure quality and accuracy
Clean and prepare your data through normalization, transformation, and feature selection.
- Choose appropriate algorithms based on your task type (classification, regression) - Configure hyperparameters to optimize model performance during training.
Define metrics to evaluate your model's performance, such as accuracy or F1 score
- Plan how to deploy your trained model into production - Consider monitoring tools to track performance and ensure reliability in real-world scenarios
Continuously monitor and refine your model based on new data and feedback.