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.
Introduction to Vertex Forecasting and Time Series in Practice
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Introduction to Vertex Forecasting and Time Series in Practice
Instructor: Google Cloud Training
Included with 
Gain insight into a topic and learn the fundamentals.
Advanced level
Designed for those already in the industry
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the key concepts and the applications of a sequence model, time series, and forecasting.
Identify the options to develop a forecasting model on Google Cloud.
Describe the workflow to develop a forecasting model by using Vertex AI.
Build a forecasting solution from end-to-end using a retail dataset.
Details to know
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Assessments
7 assignments
Taught in English
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There are 10 modules in this course
This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.
What's included
1 video1 reading
This module provides a theoretical foundation of types of sequence models, time series patterns and analysis, and forecasting notations.
What's included
6 videos1 reading1 assignment
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.
What's included
7 videos1 reading1 assignment1 app item
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.
What's included
6 videos1 reading1 assignment
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.
What's included
7 videos1 reading1 assignment1 app item
This module describes the training data split, demonstrates the evaluation metrics, and recommends the approaches to improve the model performance.
What's included
6 videos1 reading1 assignment
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.
What's included
5 videos1 reading1 assignment
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.
What's included
6 videos1 reading1 assignment1 app item
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.
What's included
7 videos1 reading1 app item
This module addresses the main features of Vertex AI Forecast and summarizes the main topics of each module.
What's included
1 video1 reading
Instructor
Offered by
Recommended if you're interested in Cloud Computing
Fractal Analytics
Google Cloud
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.