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Understanding business objectives and developing models that help to achieve them,along with metrics to track their progressManaging available resources such as hardware, data, and personnel so that deadlinesare metAnalysing the ML algorithms that could be used to solve a given problem and rankingthem by their success probabilityExploring and visualizing data to gain an understanding of it, then identifyingdifferences in data distribution that could affect performance when deploying the modelin the real worldVerifying data quality, and/or ensuring it via data cleaningSupervising the data acquisition process if more data is neededDefining validation strategiesDefining the pre-processing or feature engineering to be done on a given datasetDefining data augmentation pipelinesTraining models and tuning their hyper parametersAnalysing the errors of the model and designing strategies to overcome themDeploying models to production