Strategy & Tactics

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  1. Think backwards
  2. Build a data pipeline before a model
  3. Deliver actions over accuracy
  4. Modularise and abstract
  5. Brand the solution


Artificial Intelligence is great for complex problems where traditional approaches are difficult or impossible to implement such as long list of rules would be required or when the conditions are always changing.

Application Kaggle competition
Translate a business question into a data question.  
Validate assumptions and methods  
Consider how a machine learning model can connect to a (existing) tech stack  
Determine data sources  
Extract Data; SQL queries (sometimes across multiple databases), third-party systems, web scraping, API’s or data from partners Download some data (probably one or several CSV files)
Transforming and cleaning data; removing erroneous data, outliers and handling missing values Perhaps do a little cleaning, or chances are the data set may already be clean enough
Exploratory data analysis & feature extraction  
Feature engineering (vast range of variables) Feature engineering (a finite number of variables)
Perform preprocessing such as converting categorical data into numerical data Perform preprocessing such as converting categorical data into numerical data
Model selection; best model that can be integrated into the existing tech stack with the least amount of engineering Model selection
Build the model Build the model
Train the model Train the model
Validate the model Validate the model
Test the model Test the model
Optimise the model until it is ‘good enough’ considering the business value; perform hyperparameter tuning and compare results Run the data through a variety of suitable models until you find the best one; perform hyperparameter tuning and compare results
If new tech stack…  
- Review prior art and available libraries, services, scafolding tools  
- Implement and test tech stack  
Deploy the model  
Monitor the model in production  
Retrain when/if necessary