GCP300VERTEXFORECAST
Vertex Forecasting and Time Series in Practice
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.
What you will learn
- Understand the main 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.
- Prepare data (including ingestion and feature engineering) by using BigQuery and Vertex managed datasets.
- Train a forecasting model and evaluate the performance by using AutoML.
- Deploy and monitor a forecasting model by using Vertex AI Pipelines.
- Build a forecasting solution from end-to-end using a retail dataset.
Prerequisites
- Basic knowledge of Python syntax
- Basic understanding of machine learning models
- Prior experience building machine learning solutions on Google Cloud
Target audience
- Professional data analysts, data scientists, and ML engineers who want to build end-to-end high performance forecasting solutions on Google Cloud and add automation to the workflow.
Training Program
10 modules to master the fundamentals
Objectives
- Identify the reasons to learn Vertex AI Forecasting from Google.
- Learn the course objectives.
Topics covered
- →This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.
Objectives
- Identify the different types of sequence models.
- Identify the different patterns and analysis methods of time series.
- Describe the primary notations of forecasting.
Topics covered
- →This module provides a theoretical foundation of types of sequence models, time series patterns and analysis, and forecasting notations.
Activities
Quiz
Objectives
- Identify the options to develop forecasting models on Google Cloud.
- Describe Vertex AI and its benefits.
- Explore the workflow to build a forecasting model by using Vertex AI.
Topics covered
- →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.
Activities
Lab: Building Demand Forecasting with BigQuery ML
Quiz
Objectives
- Prepare the input data to fit the requirements of Vertex AI Forecasting.
- Demonstrate different types of features.
- Describe the best practices for the data ingestion stage.
Topics covered
- →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.
Activities
Quiz
Objectives
- Configure model training.
- Select the appropriate training optimization objective.
Topics covered
- →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.
Activities
Lab: Training a Model with Vertex AI Forecast
Quiz
Objectives
- Demonstrate training data split in time series forecasting.
- Describe evaluation metrics.
- Design the approach to improve the performance.
Topics covered
- →This module describes the training data split, demonstrates the evaluation metrics, and recommends the approaches to improve the model performance.
Activities
Quiz
Objectives
- Deploy the forecasting model.
- Describe Vertex AI Pipelines and MLOps
- Use batch predictions to generate model forecasts.
Topics covered
- →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.
Activities
Quiz
Objectives
- Describe model drift.
- Demonstrate model retraining.
- Use Vertex AI Pipelines and prebuilt (SDKs) to automate the forecasting workflow.
Topics covered
- →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.
Activities
Lab (optional): Building a Forecasting Pipeline with Vertex AI Python SDKs
Quiz
Objectives
- Describe the steps and considerations of building a forecasting solution in retail.
- Demonstrate the model development with different datasets.
- Identify the challenges and the lessons of developing a forecasting model in retail.
Topics covered
- →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.
Activities
Lab: Developing an End-to-end Forecasting Solution in Retail
Objectives
- Summarize the steps to build a forecasting model with Vertex AI.
Topics covered
- →This module addresses the main features of Vertex AI Forecast and summarizes the main topics of each module.
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote