This diagram illustrates the cyclical nature of building machine learning solutions. It highlights each major stage involved from defining a problem to monitoring a deployed model.
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Problem Definition Clearly define the business or research problem you aim to solve with machine learning.
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Data Collection Gather relevant raw data from various sources.
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Data Cleaning and Preprocessing Handle missing values, outliers, inconsistent formats, and prepare data for analysis.
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Exploratory Data Analysis (EDA) Understand the data distribution, trends, correlations, and detect anomalies.
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Feature Engineering and Selection Create, select, and transform variables that improve model performance.
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Model Selection Choose the right algorithms based on problem type and data characteristics.
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Model Training Train the selected model(s) on training data to learn patterns.
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Model Evaluation and Tuning Assess model performance using validation metrics, and fine-tune hyperparameters.
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Model Deployment Deploy the best-performing model into a production environment.
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Model Monitoring and Maintenance Continuously monitor the model’s performance and update it when needed.
Model development is not linear; it’s iterative. You may revisit earlier stages multiple times based on insights gained during evaluation or deployment.