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Key components to consider when designing ML systems include:
# Problem Definition: Clearly define the problem, data requirements, and evaluation metrics. Success criteria often involve accuracy, latency, and scalability.
# Data Pipeline: Build automated pipelines to collect, clean, transform, and validate data.
# Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural networks)

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