1: Data Preparation
Involves: Writing SQL queries, reading from SQL, transforming into clear tabular form, encoding values and imputing values
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2: Feature Selection
Determine which features are the most important. Which feature to add? Get client input.
3: Train and Evaluate
Prepare model training steps, training of multiple models, compare predictive performance of models.
4: Productionize Model
Data processing steps loaded into reproducible pipelines in your favourite cloud service. Deployment of the best trained model. Data factories, CI/CD pipelines...
5: Productionize Training
Load the training process into, for example, Azure ML. Retrain the model automatically at regular intervals.
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Machine Learning in action
