ML Deployment

This lecture will start looking into the data dimension of the ML concept and will emphasise on the importance of data-orientation. We first define the concept of data, the associated challenges, and provide examples of data collection processes. We then define a data science methodology to iteratively build the datasets that will feed our machine learning models. This lecture explores the first step of this methodology, "data access".