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Vertex Forecasting and Time Series in Practice

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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

Course Introduction
Reading list

Time series and forecasting fundamentals

Introduction
Sequence models
Time series patterns
Time series analysis
Forecasting notations
Summary
Quiz
Reading list

Forecasting options on Google Cloud

Introduction
Forecasting options
Forecasting with BigQuery ML
Vertex AI
Vertex AI Forecast workflow
Lab introduction
Summary
Quiz
Reading list
Building Demand Forecasting with BigQuery ML

Data preparation

Introduction
Data upload
Data conversion
Feature engineering
Data preparation best practices
Summary
Quiz
Reading list

Model training

Introduction
Training setup
Context window and forecast horizon
Optimization objectives
Lab introduction
Summary
Quiz
Reading list
Training a model with Vertex AI Forecast

Model evaluation

Introduction
Training data split
Backtesting
Evaluation metrics
Model improvement
Summary
Quiz
Reading list

Model deployment

Introduction
MLOps with Vertex AI Pipelines
Making predictions
Using predictions
Summary
Quiz
Reading list

Model monitoring

Introduction
Model drift
Model retraining
Pipeline automation
Lab introduction
Summary
Quiz
Reading list
Building a Forecasting Pipeline Using Vertex AI Python SDKs

Vertex forecasting in retail

Introduction
Use case background
Steps and considerations
Pilot study
Lessons
Lab introduction
Summary
Reading list
Developing an End-to-end Forecasting Solution in Retail

Summary

Course summary
Reading list

Your Next Steps

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