Introduction to Vertex Forecasting and Time Series in Practice

Overview

Class Central Tips

This course is an introduction to building forecasting solutions with Google Cloud. You start with sequence models and time series foundations. You then walk through an end-to-end workflow: from data preparation to model development and deployment with Vertex AI. Finally, you learn the lessons and tips from a retail use case and apply the knowledge by building your own forecasting models.

Syllabus

Introduction

This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.

Time series and forecasting fundamentals

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

Forecasting options on Google Cloud

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.

Data preparation

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.

Model training

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.

Model evaluation

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

Model deployment

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.

Model monitoring

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.

Vertex forecasting in retail

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.