What's missing in AutoML is the Auto

The Machine Learning community is gradually moving away from handcrafted modeling. An abundance of hyperparameter tuning linked to an equally abundant set of potential modeling techniques gave rise to AutoML, bringing automation into this process.

Does AutoML fill in all the gaps necessary to automate machine learning processes? And does AutoML answer to the challenges of time-series analysis, so persistent in many use cases? We answer these questions and more during this time for time series session.

 

 

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Time for Time Series dives into specific features and looks at general functionalities in TIM, as well as covering many topics related to time-series analysis. We welcome everyone, from managers to data scientists, to get to know TIM and to ask your questions to our experts during the live Q&A.