Overview of Vertex Forecasting and Time Series in Practice

This instructor-led course introduces learners to building forecasting solutions using Google Cloud and Vertex AI. Participants will explore time series analysis concepts, sequence models, forecasting methodologies, and machine learning workflows for predictive analytics. 

The course covers the complete forecasting lifecycle including data preparation, feature engineering, model training, evaluation, deployment, monitoring, and automation using Vertex AI Forecasting and Vertex AI Pipelines. Learners will also explore forecasting solutions using BigQuery ML and gain practical insights through a retail forecasting use case. 

Through guided labs, quizzes, demonstrations, and real-world forecasting scenarios, participants will gain practical experience building scalable end-to-end forecasting solutions on Google Cloud.

 

After Completing Vertex Forecasting and Time Series in Practice students will be able to:

  • Understand sequence models, time series analysis, and forecasting concepts.
  • Identify forecasting solution options available on Google Cloud.
  • Describe the workflow for developing forecasting models using Vertex AI.
  • Prepare and transform forecasting datasets using BigQuery and Vertex AI datasets.
  • Perform feature engineering for time series forecasting workflows.
  • Train forecasting models using Vertex AI Forecast (AutoML).
  • Evaluate forecasting model performance using industry metrics.
  • Deploy forecasting models using Vertex AI Pipelines.
  • Monitor forecasting models and implement retraining workflows.
  • Build complete end-to-end forecasting solutions using retail datasets.

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Key Features of Vertex Forecasting and Time Series in Practice

  • Advanced Forecasting and Time Series Training

  •  Hands-On Learning Experience 

  • Vertex AI Forecasting and AutoML 

  • BigQuery ML and Forecasting Options 

  •  Model Training and Evaluation 

  • Deployment, Monitoring, and MLOps

  • Retail Forecasting Use Cases

  • Google Cloud Forecasting Ecosystem Exposure 

Who should Attend Vertex Forecasting and Time Series in Practice?

  • Data Analysts
  • Data Scientists
  • ML Engineers
  • Predictive Analytics Professionals
  • Cloud AI Practitioners
  • Professionals building forecasting and time series solutions

Prerequisites of Vertex Forecasting and Time Series in Practice

  • Basic knowledge of Python syntax
  • Basic understanding of machine learning models
  • Prior experience building machine learning solutions on Google Cloud is recommended
  • Interest in predictive analytics and forecasting workflows

    • Why Choose CloudThat as your training partner for Vertex Forecasting and Time Series in Practice?

    • Specialized GCP AI and Analytics Focus- CloudThat specializes in cloud, analytics, and AI technologies and delivers focused Google Cloud training programs with practical enterprise forecasting use cases.
    • Industry-Recognized Trainers- Our trainers are certified Google Cloud professionals with expertise in Vertex AI, forecasting, machine learning, predictive analytics, and MLOps workflows. 
    • Hands-On Learning Approach- CloudThat emphasizes practical learning through guided labs, forecasting exercises, pipeline automation workflows, and real-world predictive analytics implementation scenarios.
    • Customized Learning Paths- Training programs are designed for data scientists, ML engineers, analysts, and cloud professionals with varying levels of forecasting and AI expertise.
    • Interactive Learning Experience- Sessions include demonstrations, collaborative forecasting exercises, troubleshooting activities, quizzes, and interactive discussions. 
    • Placement Assistance and Career Support- CloudThat supports learners with AI learning paths, interview preparation, forecasting implementation guidance, and career development support. 
    • Continuous Learning and Updates- Course content is continuously updated to align with the latest advancements in Vertex AI, forecasting solutions, predictive analytics, and MLOps practices.
    • Positive Reviews and Testimonials- Thousands of professionals and enterprises trust CloudThat for advanced cloud, AI, analytics, forecasting, and Google Cloud training programs.

    Learning Objectives of Vertex Forecasting and Time Series in Practice

    • This course enables learners to build scalable and automated forecasting solutions on Google Cloud using Vertex AI Forecast, BigQuery ML, Vertex AI Pipelines, and time series modeling techniques.   

    Course Outline for Vertex Forecasting and Time Series in Practice Download Course Outline

    Lecture Content

    • Reasons to Build Forecasting Solutions on Google Cloud
    • Course Objectives and Learning Path
    • Introduction to Forecasting Workflows

    Learning Objectives

    • Understand the business value of forecasting solutions
    • Explore forecasting capabilities on Google Cloud
    • Understand course goals and learning outcomes

    Lecture Content

    • Sequence Models
    • Time Series Patterns and Analysis
    • Forecasting Notations
    • Forecasting Concepts and Terminology

    Learning Objectives

    • Identify different types of sequence models
    • Understand time series patterns and analysis techniques
    • Describe forecasting notations and terminology
    • Explore forecasting concepts for predictive analytics

    Activities

    • Quiz

    Lecture Content

    • BigQuery ML Forecasting
    • Vertex AI Forecast (AutoML)
    • Vertex AI Features and Benefits
    • End-to-End Forecasting Workflow with AutoML

    Learning Objectives

    • Compare forecasting options on Google Cloud
    • Understand Vertex AI Forecast workflows
    • Explore forecasting model development using Vertex AI
    • Build forecasting models using BigQuery ML

    Lab Content

    • Building Demand Forecasting with BigQuery ML

    Activities

    • Quiz

    Lecture Content

    • Preparing Data for Vertex AI Forecasting
    • Feature Engineering for Time Series
    • Time Series Feature Types
    • Data Ingestion Best Practices

    Learning Objectives

    • Prepare datasets for forecasting workflows
    • Understand different feature types in time series models
    • Explore best practices for forecasting data ingestion
    • Perform feature engineering for predictive analytics

    Activities

    • Quiz

    Lecture Content

    • Forecasting Model Training Workflows
    • Context Window Configuration
    • Forecast Horizon Configuration
    • Training Optimization Objectives

    Learning Objectives

    • Configure forecasting model training
    • Select appropriate optimization objectives
    • Understand context window and forecast horizon concepts
    • Train forecasting models using Vertex AI Forecast

    Lab Content

    • Training a Model with Vertex AI Forecast

    Activities

    • Quiz

    Lecture Content

    • Training Data Splits
    • Forecasting Evaluation Metrics
    • Performance Improvement Strategies
    • Forecast Accuracy Analysis

    Learning Objectives

    • Understand time series training data split strategies
    • Evaluate forecasting models using industry metrics
    • Improve model performance and forecast accuracy
    • Analyze forecasting outcomes and predictions

    Activities

    • Quiz

    Lecture Content

    • Batch Prediction Workflows
    • Deploying Forecasting Models
    • Vertex AI Pipelines and MLOps
    • Production Deployment Concepts

    Learning Objectives

    • Deploy forecasting models using Vertex AI
    • Generate forecasts using batch prediction workflows
    • Understand Vertex AI Pipelines and MLOps concepts
    • Transition forecasting models from development to production

    Activities

    • Quiz

    Lecture Content

    • Model Drift Concepts
    • Forecast Model Retraining
    • Automating Forecasting Workflows
    • Vertex AI Pipelines Automation

    Learning Objectives

    • Understand model drift and retraining workflows
    • Automate forecasting pipelines using Vertex AI Pipelines
    • Explore prebuilt SDKs for workflow orchestration
    • Monitor forecasting models in production environments

    Lab Content

    • Optional Lab: Building a Forecasting Pipeline with Vertex AI Python SDKs

    Activities

    • Quiz

    Lecture Content

    • Retail Forecasting Use Cases
    • Forecasting Workflow Design in Retail
    • Pilot Studies with Multiple Datasets
    • Forecasting Challenges and Lessons Learned

    Learning Objectives

    • Understand forecasting workflows in retail environments
    • Explore model development using retail datasets
    • Compare forecasting approaches using different datasets
    • Identify forecasting implementation challenges and solutions

    Lab Content

    • Developing an End-to-End Forecasting Solution in Retail

    Lecture Content

    • Vertex AI Forecast Features Review
    • Forecasting Workflow Recap
    • Summary of Key Concepts and Best Practices

    Learning Objectives

    • Summarize forecasting workflows using Vertex AI
    • Review major concepts covered throughout the course
    • Reinforce best practices for forecasting solutions

    Lab Content

    • Final Discussion / Wrap-up

    Certification Detail of Vertex Forecasting and Time Series in Practice

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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    Course ID: 28691

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    FAQs for Vertex Forecasting and Time Series in Practice

    This course is designed for data analysts, data scientists, and ML engineers interested in building forecasting solutions on Google Cloud.

    Yes. Prior experience with machine learning workflows on Google Cloud is recommended.

    The course covers time series analysis, forecasting concepts, Vertex AI Forecast, BigQuery ML, feature engineering, model training, deployment, monitoring, and MLOps workflows.

    Yes. The course includes labs involving BigQuery ML, Vertex AI Forecast, Vertex AI Pipelines, and retail forecasting workflows.

    Vertex AI, Vertex AI Forecast (AutoML), BigQuery ML, Vertex AI Pipelines, and TensorFlow.

    Yes. Learners will explore Vertex AI Pipelines, automation workflows, model monitoring, and retraining concepts.

    Yes. The course includes batch prediction workflows and forecasting model deployment using Vertex AI.

    Instructor-led or on-demand training with lectures, labs, quizzes, demonstrations, and forecasting exercises.

    Yes. A CloudThat Course Completion Certificate will be awarded after successful completion of the training.

    Yes. This is an advanced-level course designed for professionals with prior machine learning and cloud analytics experience.

    Enquire Now