This module provides a theoretical foundation of types of sequence models, time series patterns and analysis, and forecasting notations.

This module introduces two major options to build a forecasting solution on Google Cloud: BigQuery ML and Vertex AI Forecast (AutoML). It also investigates the unique features of Vertex AI Forecast and explores an end-to-end workflow with AutoML.

This module explores the transformation of original data to the data types and format supported by Vertex AI. It also introduces the different types of features in time series and the best practices for data ingestion.

This module walks learners through the model training and demonstrates the configuration details such as the setup of context window, forecast horizon, and optimization objective.

This module describes the training data split, demonstrates the evaluation metrics, and recommends the approaches to improve the model performance.

This module demonstrates model prediction, specifically the batch prediction with Vertex AI Forecast. It also explores machine learning operations (MLOps) and the transition from development to production.

This module describes model drift and the approach of model retraining. It also demonstrates the automation of the forecasting workflow by using Vertex AI Pipelines.

This module describes a use case to build a forecasting solution with Vertex AI Forecast in a retail store. It demonstrates the steps and considerations, walks through a pilot study with two different datasets, and discusses the challenges and lessons.

This module addresses the main features of Vertex AI Forecast and summarizes the main topics of each module.