Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand.

Building machine learning models can be a time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering.

These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.

Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.

This session will highlight the forecasting features of Automated ML and how to leverage them.

Time index:

  • [00:35] – What is time-series forecasting?
  • [01:30] – Simplify ML with Automated ML
  • [02:30] – DriveTime customer scenario
  • [04:15] – Features & Functionality
  • [05:20] – Demo

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In this video, Siraj Raval explores Automatic Machine Learning or “AutoML,” a field of Artificial Intelligence that’s gaining a lot of ground of late. The idea is that doing any kind of task related to machine learning involves a whole lot of steps like cleaning a dataset, choosing a model, deciding what the right configurations of that model should be, deciding what the most relevant features are etc.

From the video description:

The goal of AutoML is to automate all of that up to a point where all a data scientist would need to do is tell a machine to perform some task using a dataset and wait for it to learn how by itself. In this episode, i’m going to explain several popular AutoML techniques, then compare top AutoML frameworks like AutoKeras, Auto Sklearn, h20, Ludwig, etc. to help you decide which one will be the best for your needs.