- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress. - Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability. - Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world. - Verifying data quality, and/or ensuring it via data cleaning. - Supervising the data acquisition process if more data is needed. - Defining validation strategies and the preprocessing or feature engineering to be done on a given dataset. - Training models and tuning their hyperparameters. - Analyzing the errors of the model and designing strategies to overcome them. - Deploying models to production.
