T-VFTS-AOfficial Google Curriculum
Introduction to Vertex Forecasting and Time Series in Practice
1 dayILTAdvancedLoading...
Overview
Introduction to building forecasting solutions with Google Cloud covering sequence models, time series foundations, and end-to-end workflow from data preparation to model development and deployment with Vertex AI.
What You'll 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
Who Should Attend
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
Prerequisites
Having one or more of the following: Basic knowledge of Python syntax. Basic understanding of machine learning models. Prior experience building machine learning solutions on Google Cloud.
Products Covered
Vertex AIAutoMLBigQuery MLVertex AI PipelinesTensorFlow
Course Modules
1
Course Introduction
Topics
- This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives
Learning Outcomes
- Identify the reasons to learn Vertex AI Forecasting from Google
- Learn the course objectives
2
Time Series and Forecasting Fundamentals
Topics
- This module provides a theoretical foundation of types of sequence models, time series patterns and analysis, and forecasting notations
Learning Outcomes
- Identify the different types of sequence models
- Identify the different patterns and analysis methods of time series
- Describe the primary notations of forecasting
Activities
Quiz
3
Forecasting Options on Google Cloud
Topics
- 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
Learning Outcomes
- 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
Activities
Lab: Building Demand Forecasting with BigQuery MLQuiz
4
Data Preparation
Topics
- 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
Learning Outcomes
- 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
Activities
Quiz
5
Model Training
Topics
- 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
Learning Outcomes
- Configure model training
- Select the appropriate training optimization objective
Activities
Lab: Training a Model with Vertex AI ForecastQuiz
6
Model Evaluation
Topics
- This module describes the training data split, demonstrates the evaluation metrics, and recommends the approaches to improve the model performance
Learning Outcomes
- Demonstrate training data split in time series forecasting
- Describe evaluation metrics
- Design the approach to improve the performance
Activities
Quiz
7
Model Deployment
Topics
- 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
Learning Outcomes
- Deploy the forecasting model
- Describe Vertex AI Pipelines and MLOps
- Use batch predictions to generate model forecasts
Activities
Quiz
8
Model Monitoring
Topics
- 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
Learning Outcomes
- Describe model drift
- Demonstrate model retraining
- Use Vertex AI Pipelines and prebuilt (SDKs) to automate the forecasting workflow
Activities
Lab (optional): Building a Forecasting Pipeline with Vertex AI Python SDKsQuiz
9
Vertex Forecasting in Retail
Topics
- 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
Learning Outcomes
- 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
Activities
Lab: Developing an End-to-end Forecasting Solution in Retail
10
Course Summary
Topics
- This module addresses the main features of Vertex AI Forecast and summarizes the main topics of each module
Learning Outcomes
- Summarize the steps to build a forecasting model with Vertex AI
Get This Training
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Course Details
- Course Code
- T-VFTS-A
- Duration
- 1 day
- Format
- ILT
- Level
- Advanced
- Modules
- 10
- Activities
- 11
- Price
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