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Google Cloud Training
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.
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OpenCourser.com/course/iov5ws/vertex
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What's inside
Syllabus
Introduction
This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.
Time series and forecasting fundamentals
Read more
Syllabus
Introduction
This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.
Time series and forecasting fundamentals
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops skills that are highly sought after in the data science industry
Utilizes real-world retail data, showcasing practical applications
Suitable for learners starting their journey in time series forecasting
Offers hands-on experience through model building and deployment
Leverages Google Cloud's Vertex AI platform, providing industry-standard tools
Covers both theoretical foundations and practical implementation
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Reviews summary
Practical vertex ai forecasting fundamentals
Learners say this course provides a solid introduction to building forecasting solutions with Google Cloud's Vertex AI. The end-to-end workflow, from data preparation to model deployment and monitoring, is frequently praised for its practicality. Many highlight the strong focus on Vertex AI Forecast (AutoML) as a key asset for professionals. While time series fundamentals are covered, some suggest a prior understanding of machine learning concepts is beneficial. The hands-on approach, including a valuable retail use case, makes the learning highly applicable. Overall, it is considered a practical and relevant course for cloud-based ML practitioners.
Real-world case study provides practical application context.
"The retail use case helped solidify my understanding of how these concepts apply in a business scenario."
"It was great to see the forecasting solution built out with real-world considerations."
"The case study provided an excellent example of applying Vertex AI Forecast effectively."
Good coverage of operationalizing models and monitoring.
"The sections on model deployment, monitoring, and MLOps are highly relevant for production environments."
"I found the module demonstrating batch prediction and Vertex AI Pipelines for automation very valuable."
"Learning about model drift and retraining strategies ensures I can maintain my forecasting solutions effectively."
Provides deep, practical insights into Vertex AI's AutoML.
"I gained a solid understanding of how to leverage Vertex AI Forecast (AutoML) for my time series projects."
"The detailed explanations of Vertex AI's features for forecasting were exactly what I needed."
"It's a great course if you specifically want to learn about Google Cloud's AutoML forecasting capabilities."
Offers a comprehensive, real-world forecasting workflow.
"The end-to-end workflow with Vertex AI, from data prep to MLOps, was incredibly practical and applicable."
"I appreciated the step-by-step guidance on building a complete forecasting solution using Google Cloud."
"This course helped me understand the full lifecycle of a forecasting project, which is essential for my role."
Introduces time series basics, but benefits from prior ML.
"The time series and forecasting fundamentals were helpful, but some prior ML knowledge really helps grasp the concepts faster."
"I found the initial modules a bit fast-paced; it's more for those with some exposure to ML concepts."
"While the course covers basics, it moves quickly, so coming in with an understanding of sequence models is advisable."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Vertex Forecasting and Time Series in Practice with these
activities:
Read 'Forecasting: Principles and Practice'
Show steps
This book provides a comprehensive overview of forecasting methods and will help you build a strong foundation in the subject.
View
Forecasting: Principles and Practice
on Amazon
Show steps
-
Read Chapters 1-3 to gain an understanding of the basics of forecasting.
-
Work through the examples in Chapter 4 to practice applying forecasting methods.
-
Complete the exercises at the end of each chapter to test your understanding.
Join a study group for this course
Show steps
This study group will provide you with an opportunity to collaborate with other students and learn from each other.
Show steps
-
Find a study group for this course.
-
Attend the study group meetings.
-
Participate in the discussions.
Solve forecasting practice problems
Show steps
These practice problems will help you to develop your skills in forecasting.
Browse courses on
Time Series Forecasting
Show steps
-
Find a set of forecasting practice problems online.
-
Attempt to solve the problems on your own.
-
Check your answers against the provided solutions.
One other activity
Expand to see all activities and additional details
Show all four activities
Build a forecasting model for a real-world dataset
Show steps
This project will give you hands-on experience in building and evaluating a forecasting model.
Browse courses on
Time Series Modeling
Show steps
-
Choose a dataset that you are interested in forecasting.
-
Explore the data and identify any trends or patterns.
-
Select a forecasting algorithm and train a model on the data.
-
Evaluate the performance of the model using metrics such as MAE and RMSE.
-
Deploy the model and use it to make predictions.
Read 'Forecasting: Principles and Practice'
Show steps
This book provides a comprehensive overview of forecasting methods and will help you build a strong foundation in the subject.
View
Forecasting: Principles and Practice
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to gain an understanding of the basics of forecasting.
- Work through the examples in Chapter 4 to practice applying forecasting methods.
- Complete the exercises at the end of each chapter to test your understanding.
Join a study group for this course
Show steps
This study group will provide you with an opportunity to collaborate with other students and learn from each other.
Show steps
Show steps
Show steps
- Find a study group for this course.
- Attend the study group meetings.
- Participate in the discussions.
Solve forecasting practice problems
Show steps
These practice problems will help you to develop your skills in forecasting.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Find a set of forecasting practice problems online.
- Attempt to solve the problems on your own.
- Check your answers against the provided solutions.
One other activity
Expand to see all activities and additional details
Show all four activities
Build a forecasting model for a real-world dataset
Show steps
This project will give you hands-on experience in building and evaluating a forecasting model.
Browse courses on
Time Series Modeling
Show steps
Show steps
Show steps
- Choose a dataset that you are interested in forecasting.
- Explore the data and identify any trends or patterns.
- Select a forecasting algorithm and train a model on the data.
- Evaluate the performance of the model using metrics such as MAE and RMSE.
- Deploy the model and use it to make predictions.
Career center
Learners who complete Introduction to Vertex Forecasting and Time Series in Practice will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts study data using specialized applications and statistical techniques. They analyze data to help businesses understand trends, improve performance, and make better decisions. This course helps build a foundation for Data Analysts because it teaches the fundamental principles of time series forecasting, which is a critical skill for analyzing data over time. The course also introduces learners to Vertex AI, a powerful cloud-based platform that provides tools and services for building and deploying machine learning models.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and computer science to extract insights from data. They develop and implement machine learning models to solve business problems. This course may be useful for Data Scientists who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work closely with Data Scientists and other engineers to develop and implement machine learning solutions. This course may be useful for Machine Learning Engineers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They help businesses make investment decisions and manage risk. This course may be useful for Quantitative Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Business Analyst
Business Analyst
Business Analysts study business processes and identify ways to improve them. They use data analysis to understand customer needs and develop strategies for growth. This course may be useful for Business Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Financial Analyst
Financial Analyst
Financial Analysts study financial data to identify investment opportunities and make recommendations to clients. They use a variety of techniques to analyze data, including time series forecasting. This course may be useful for Financial Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Market Researcher
Market Researcher
Market Researchers study market trends and customer behavior. They use data analysis to identify opportunities for new products and services. This course may be useful for Market Researchers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work with businesses to improve efficiency and productivity. This course may be useful for Operations Research Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be useful for Product Managers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses. They use data analysis to develop strategies for managing risk. This course may be useful for Risk Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Sales Analyst
Sales Analyst
Sales Analysts study sales data to identify trends and opportunities. They use data analysis to develop strategies for increasing sales. This course may be useful for Sales Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical methods to develop models and make predictions. This course may be useful for Statisticians who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
System Analyst
System Analyst
System Analysts design and implement computer systems. They work with businesses to identify their needs and develop systems that meet those needs. This course may be useful for System Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. They use data analysis to identify areas for improvement. This course may be useful for User Experience Researchers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
Web Analyst
Web Analyst
Web Analysts study website traffic and user behavior. They use data analysis to identify ways to improve the user experience and increase website traffic. This course may be useful for Web Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.
For more career information including salaries, visit:
OpenCourser.com/course/iov5ws/vertex
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Vertex Forecasting and Time Series in Practice.
Save
This textbook provides a comprehensive and accessible introduction to forecasting methods, with a focus on practical applications. It covers a wide range of topics, including time series decomposition, ARIMA models, and exponential smoothing.
Provides a practical guide to using Python for time series forecasting. It covers a wide range of topics, including data preprocessing, model selection, and model evaluation.
Save
This textbook provides a comprehensive and rigorous treatment of time series analysis, with a focus on statistical methods. It valuable resource for students and professionals seeking a deep understanding of time series analysis.
This textbook provides a comprehensive and accessible introduction to statistical learning, with a focus on supervised learning methods. It valuable resource for students and professionals seeking a deeper understanding of statistical learning.
This textbook provides a comprehensive and rigorous treatment of pattern recognition and machine learning, with a focus on theoretical foundations. It valuable resource for students and professionals seeking a deep understanding of pattern recognition and machine learning.
This textbook provides a comprehensive and rigorous treatment of machine learning, with a focus on probabilistic models. It valuable resource for students and professionals seeking a deep understanding of machine learning.
This textbook provides a comprehensive and accessible introduction to Bayesian data analysis, with a focus on practical applications. It valuable resource for students and professionals seeking a deeper understanding of Bayesian data analysis.
This textbook provides a comprehensive and accessible introduction to statistical learning, with a focus on practical applications in R. It valuable resource for students and professionals seeking a deeper understanding of statistical learning.
Provides a comprehensive and accessible introduction to machine learning, with a focus on practical applications. It valuable resource for beginners seeking to understand the fundamental concepts of machine learning.
This textbook provides a comprehensive and rigorous treatment of time series analysis, with a focus on applications in various fields. It valuable resource for students and professionals seeking a deep understanding of time series analysis.
This textbook provides a comprehensive and accessible introduction to time series analysis, with a focus on practical applications. It valuable resource for students and professionals seeking a deeper understanding of time series analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iov5ws/vertex
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