: Plan for model deployment, orchestration, and continuous monitoring for issues like data drift. Key Case Studies

Choose the right ML task (e.g., classification vs. ranking). Data Preparation: Design the data pipeline, including collection and feature engineering Model Development: Select algorithms and training strategies. Evaluation: Define offline and online metrics like accuracy or latency. Design for deployment, scaling, and real-time inference. Monitoring: Implement mechanisms for tracking model decay and handling data bias Key Case Studies

as a specific machine learning task (e.g., classification, ranking).