Bengaluru, Karnataka, India, India
July 3, 2024
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- 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
- Supervising the data acquisition process if more data is needed
- 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
- Deploying models to production
- Finding available datasets online that could be used for training
- Defining validation strategies
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyperparameters
- Analyzing the errors of the model and designing strategies to overcome them
