Stage 1.
Align and plan.
Understand business objectives. Write specification, determine task type (classification or regression), assess the data, and define success metrics. Estimate and plan.
Stage 2.
Prepare data.
Feature engineering, data mining, data scraping, data augmentation and/or dimensionality reduction, visualization with histograms and scatter plots.
Stage 3.
Develop model.
Experiment with various architectures. Optimize hyperparameters, address overfitting/underfitting. Evaluate using metrics, use cross-validation for robustness.
Stage 4.
Deploy and maintain.
Choose between cloud or on-premise deployment. Ensure scalability. Monitor real-time performance. Continuously fine-tune with new data.