For every data / data science use case

Step #1 - Build Reporting

• Consider Dashboards for key metrics, KPIs
• Spot daily business / Trends

Step #2 - Data Exploration to Understand data

• Learn domain, Explore it
• Spot features outside your data
• Build insights from your data
• Scrap external data like local demographics data

Step #3 - Model Considerations @ Lowest level or Enterprise level

• Consider building Global and local model
• Different algos and outputs, Consider ensemble or one model depending on model performance
• Visualize models with interpretation
• Overlay charts for predictions/analysis
• Business knowledge guides model correlations, Constantly validate with business

Step #4 - Model Optimization / Improvements - Keep Learning

• Continuously build optimize models
• Measure Model drift comparison past to present
• Consider keeping add on variables as needed
• Evolve it
• Depending on Deployment scenario quantize / optimize to lite weight models

Step #5 - Be ready to collaborate and take business inputs in regular intervals

• Good design comes from clarity of thinking
• Customer-first approach
• Design for scalability vs get something working

Step #6 - Deployment - Ready for consumption

• Scale with expertise as needed
• Dockerize as possible
• Expose as API endpoints
• Build security
• Deploy and run as needed to minimize costs

Step #7 - Organize code, data, models

• Version data vs model built
• Keep track of patterns of data vs model improvements
• Look at the explainability aspect using LIME / SHAP

Customize the reporting layer built-in step #1 to remap model behavior vs actual data, Leverage it as patterns vs predictions vs actuals.

Keep Iterating!!!