Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines are met
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
Finding available datasets online that could be used for training
Defining validation strategies
Defining the preprocessing or feature engineering to be done o...
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