Engineering
From data to deployment.
Building machine-learning pipelines end to end—from preparing data to a deployed model.
EngineeringIDCamp ML Development
THE STORY
IDCamp ML Development
Through IDCamp’s ML Development track, I worked on end-to-end machine-learning pipelines: data, training, evaluation and deployment. It is part of a longer path into AI and machine learning, alongside my MSc in Computer Science.
Problem
A model is only useful once it works beyond the notebook. The challenge was to take a problem through every stage of a pipeline, not just the training step.
Approach
I treated the pipeline as one connected system: preparing the data, training and evaluating the model, then deploying it so it could be used.
A foundation for applying machine learning to the worlds I already work in: sport, media and community.