Machine Learning Workflow and Best Practices

Building a successful machine learning system requires more than just training a model. It involves a structured workflow from problem definition to deployment, along with best practices that prevent common mistakes and ensure reliable, reproducible results. This lesson brings everything together into a complete ML workflow.

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The End-to-End Machine Learning Workflow

Step	Description	Key Activities
1	Define the Problem	Understand the business question, define success metrics
2	Collect Data	Gather relevant data from databases, APIs, files
3	Explore Data (EDA)	Visualise distributions, correlations, missing values
4	Preprocess Data	Clean, impute, scale, encode features
5	Engineer Features	Create new features, select important ones
6	Train Models	Try multiple algorithms, use cross-validation
7	Evaluate Models	Compare metrics, analyse errors
8	Tune Hyperparameters	Grid search, randomised search
9	Deploy Model	Serve predictions in production
10	Monitor and Maintain	Track performance, retrain when needed

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