The Complete Machine Learning Process: From Problem to Deployment
Describe the end-to-end machine learning workflow from problem definition through model deployment.
Machine learning projects follow a structured lifecycle that extends far beyond model training. The process begins with problem definition and data collection, moves through exploratory analysis and feature engineering, progresses to model selection and evaluation, and concludes with deployment and monitoring. Each phase has distinct objectives and success criteria. Understanding this holistic view prevents common pitfalls such as optimizing for the wrong metric, deploying models without proper validation, or failing to account for data drift in production. The cloud-native approach using AWS SageMaker integrates all these phases into a cohesive workflow that emphasizes reproducibility and scalability.
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