Machine Learning

Machine Learning (ML) software is a software system with one or more components that learn from data. This entails engineering a pipeline for the collection and pre-processing of data, the training of an ML model, the deployment of the trained model to perform inference and the software engineering of the encompassing software system that sends new input data to the model to get answers.

This post on ML projects explains why ML projects are different from traditional rule-based software engineering and identifies eight challenges for engineering machine learning applications:

1. Data requirements engineering including data visualizations
2. ML components are more difficult to handle as distinct modules
3. Design of the ML component through algorithm selection and tuning
4. Break up the ML development in increments
5. Data and model management for the current and future projects
6. Find ML models that can be reused for your application
7. Validation of ML applications in absence of a specification to test against
8. Explainability of ML models is needed for debugging

tbc.