Building accurate machine learning models requires a deep understanding of machine learning, as well as other data science and coding skills. Automated machine learning's purpose is to alleviate these requirements, automating the ML workflow of data cleansing, feature engineering, algorithm selection, hyperparameter tuning, and deployment. Enabling business domain experts to leverage ML in their day to day work using the web UI without writing a single line of code, as well as optimizing data scientists work by automating part of the technical work.

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00:55 - Start an Automated ML Experiment
03:07 - Analyze experiment outputs and deploy recommended model

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