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.
Overview
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
- Course Badge
Tags
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