Vertex Forecasting and Time Series in Practice
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27 May 2024 - 27 May 2024 | Vertex Forecasting and Time Series in Practice |
Description
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 Verex AI. Finally, you learn the lessons and tips from a retail use case and apply the knowledge by building your own forecasting models.
Audience and prerequisites
This class is intended for the following job roles:
- 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
To get the most out of this course, participants should:
- Basic knowledge of Python syntax
- Basic understanding of machine learning models
- Prior experience building machine learning solutions on Google Cloud
Objectives
This course teaches participants the following skills:
- 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.
Topics
Module 1: Course Introduction
- Identify the reasons to learn Verex AI Forecasting from Google.
- Learn the course objectives.
Module 2: Time Series and Forecasting Fundamentals
- Identify the different types of sequence models.
- Identify the different patterns and analysis methods of time series.
- Describe the primary notations of forecasting.
Module 3: Forecasting Options on Google Cloud
- Identify the options to develop forecasting models on Google Cloud.
- Describe Verex AI and its benefits.
- Explore the workflow to build a forecasting model by using Verex AI.
Module 4: Data Preparation
- Prepare the input data to fit the requirements of Verex AI Forecasting.
- Demonstrate different types of features.
- Describe the best practices for the data ingestion stage.
Module 5: Model Training
- Configure model training.
- Select the appropriate training optimization objective.
Module 6: Model Evaluation
- Demonstrate training data split in time series forecasting.
- Describe evaluation metrics.
- Design the approach to improve the performance.
Module 7: Model Deployment
- Deploy the forecasting model.
- Describe Verex AI Pipelines and MLOps
- Use batch predictions to generate model forecasts.
Module 8: Model Monitoring
- Describe model drift.
- Demonstrate model retraining.
- Use Verex AI Pipelines and prebuilt (SDKs) to automate the forecasting workflow.
Module 9: Verex Forecasting in Retail
- 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.
Module 10: Course Summary
- Summarize the steps to build a forecasting model with Vertex AI.
Open calls
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27 May 2024 - 27 May 2024 | Vertex Forecasting and Time Series in Practice |
