Пост ML Lifecycle

ML Lifecycle 1. Identify the business goal: Define the core business objectives and target outcomes for the project. 2. Frame the ML problem: Convert the business objective into a technical machine learning problem formulation. 3. Process data: A multi-step phase spanning data preparation: 3.1. Collect data: Gather relevant raw data required for the project. 3.2. Pre-process data: Clean, format, and prepare raw data for modeling. 3.3. Engineer the features: Select, transform, and create predictive features to optimize performance. 4. Train, tune, and evaluate: Train candidate models, optimize hyperparameters, and assess accuracy against evaluation metrics. 5. Deploy: Release the validated model into a production environment to handle live inference. 6. Monitor: Continuously track post-deployment model performance, predictions, and operational health. #notes #ml_prod