Machine Learning (ML) Engineering Development Methodology
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A Machine Learning (ML) Engineering Development Methodology is a software development methodology for ML systems.
- Context:
- It can include guidelines for Data Collection, Data Preprocessing, Feature Engineering, ML Model Training, and ML Model Evaluation.
- It can range from being a Waterfall ML Development Methodology to being an Iterative ML Development Methodology (where continuous testing and refinement are integral to the process).
- It can account for collaboration between ML Engineers, Backend Engineers, Data Engineers, and Data Scientists to ensure effective integration of ML components into broader software systems.
- It can account for handling Very Large Datasets.
- It can account for ML System Maintenance, to ensure they remain functional and accurate over time and as data evolves.
- It can incorporate practices from Agile Software Development.
- It can include the assessment of Model Reliability and Ethical Model Use, especially in sensitive applications.
- It can involve the application of ML Ops (Machine Learning Operations), and incorporate DevOps practices.
- It can involve ML Model Versioning, tracking, and managing ML experiments and models.
- It can involve ML Model Interpretability, especially in sectors like healthcare and finance where understanding model decisions is crucial.
- ...
- Example(s):
- Counter-Example(s):
- A traditional software development project that follows a linear, non-iterative approach without the complexities of data handling and model training.
- A project where ML is not a central component, and hence, the development methodology does not account for the unique challenges of machine learning.
- See: Evaluation Driven Development, Data Science, Agile Software Development, DevOps, ML Ops, Predictive Analytics, Recommendation System, Fraud Detection.