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Google Cloud
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle.
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
This course is no longer available. Find a similar course by searching these:
artificial intelligence
cloud machine learning
data science
vertex ai
google cloud platform
data-to-ai lifecycle
Register for this course and see more details by visiting:
OpenCourser.com/course/hhmm8h/google
What's inside
Syllabus
Course Introduction
Big Data and Machine Learning on Google Cloud
Data Engineering for Streaming Data
Big Data with BigQuery
Read more
Syllabus
Course Introduction
Big Data and Machine Learning on Google Cloud
Data Engineering for Streaming Data
Big Data with BigQuery
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches foundational concepts and skills in big data that are essential for industry knowledge and practice
Introduces Google Cloud's Vertex AI, a platform that streamlines machine learning processes
Provides hands-on labs and interactive materials, which enhances the learning experience and strengthens practical skills
Taught by Google Cloud, which is renowned for its expertise in cloud computing and machine learning
Explores the benefits of building a big data pipeline with Google Cloud, which is highly relevant to industry trends and practices
Covers emerging technologies and tools, such as Google Cloud's Vertex AI, providing learners with cutting-edge knowledge
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Reviews summary
Foundational gcp big data & ml overview
According to learners, this course serves as an excellent introduction for beginners to Google Cloud's big data and machine learning services. Many praise the clear explanations and well-structured content, finding it easy to follow. The hands-on labs are consistently highlighted as a major strength, effectively solidifying understanding across a wide range of topics from BigQuery to Vertex AI. However, some learners with prior experience found the content a bit too superficial for deeper dives. A recurring concern is the Qwiklabs environment, with several students reporting frustrating issues like initialization problems or outdated instructions.
Covers many services but may be superficial for advanced users.
"It's perfect for beginners, but those with some prior cloud experience might find some parts too basic."
"I found this course to be too superficial. It's mostly a tour of services rather than a deep dive into how to effectively use them."
"Some topics, especially around advanced machine learning concepts, could use more depth. It's great for understanding the 'what' but less for the 'how-to'."
"I feel I'll need to do a lot of self-study to truly grasp the nuances; it's a good high-level introduction, but not enough for practical implementation."
Well-organized material with clear explanations from the instructor.
"I found the explanations clear and the hands-on labs were incredibly helpful for understanding the concepts."
"The instructor does a great job breaking down complex topics into digestible chunks."
"The material is well-structured and easy to follow. The instructor explains things clearly."
Labs enhance learning by providing practical application.
"The hands-on labs were incredibly helpful for understanding the concepts."
"The labs are interactive and solidify the learning."
"The practical labs truly enhance the learning experience, allowing me to apply what I learned immediately."
"The hands-on exercises were key for me to grasp the material and fill in some gaps."
Provides a strong, clear foundation for new learners.
"This course is an excellent introduction to Google Cloud's big data and machine learning services."
"Absolutely fantastic for beginners! The instructor does a great job breaking down complex topics into digestible chunks."
"Excellent course to get started with Big Data and ML on Google Cloud. It lays a strong groundwork for further specialization."
"As someone new to GCP, I felt very well-supported, and the pace was just right."
Some learners faced frustrating technical problems with labs.
"I encountered some issues with the Qwiklabs environment not always working smoothly, which was frustrating."
"The Qwiklabs environment also gave me persistent issues, which hindered my learning significantly."
"The Qwiklabs environment was a constant source of frustration. Many labs failed to initialize correctly or had outdated instructions."
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 Google Cloud Big Data and Machine Learning Fundamentals with these
activities:
Revisit machine learning preprocessing steps
Show steps
Reviewing machine learning preprocessing steps will refresh your knowledge and strengthen your understanding of data preparation techniques, which are crucial for successful model building.
Show steps
-
Read through your notes or textbooks on machine learning preprocessing techniques.
-
Go through online tutorials or articles that explain the different preprocessing steps in more detail.
-
Complete practice exercises or quizzes on machine learning preprocessing.
Review streaming data processing
Show steps
Review the basics of streaming data processing to ensure a strong foundation for the course.
Browse courses on
Data Pipelines
Show steps
-
Identify different data stream characteristics
-
Explore various streaming data processing tools
-
Practice setting up and managing streaming data pipelines
Review notes from previous courses
Show steps
Reread course notes and review past assignments and quizzes to strengthen your understanding of key concepts in big data and machine learning.
Browse courses on
Big Data
Show steps
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice SQL Queries
Show steps
Reinforce your knowledge of SQL queries to prepare for working with big data in Google Cloud.
Browse courses on
SQL
Show steps
-
Review SQL syntax and commands
-
Solve practice problems using SQL
Follow Google Cloud BigQuery Tutorials
Show steps
Gain hands-on experience with Google Cloud BigQuery through interactive tutorials provided by Google.
Browse courses on
BigQuery
Show steps
-
Complete the BigQuery Quickstart tutorial
-
Explore the BigQuery documentation for advanced tutorials
Compile a glossary of big data and machine learning terms
Show steps
Create a document that defines and explains important terms used in big data and machine learning to improve your understanding and recall of key concepts.
Browse courses on
Big Data
Show steps
-
Identify key terms from course materials
-
Research and write definitions
-
Organize and categorize the terms
Participate in Online Discussion Forums
Show steps
Engage with fellow learners to exchange ideas, ask questions, and clarify concepts related to big data and machine learning.
Browse courses on
Big Data
Show steps
-
Join the course discussion forum
-
Post questions and respond to your peers
Complete Google Cloud Quickstart tutorials
Show steps
Follow official Google Cloud tutorials to practice creating and querying datasets in BigQuery, gaining hands-on experience with big data tools.
Browse courses on
BigQuery
Show steps
-
Set up a Google Cloud account
-
Create a BigQuery dataset
-
Import data into BigQuery
-
Write SQL queries to retrieve data
Join a study group and assist other students
Show steps
Engage with fellow students by participating in a study group, offering support and clarification to enhance your own understanding while reinforcing concepts through teaching.
Show steps
Build a Simple Machine Learning Model with Vertex AI
Show steps
Apply your understanding of machine learning concepts by creating a simple model using Vertex AI.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset and problem to solve
-
Create a model using Vertex AI
-
Evaluate and deploy the model
Work Through Big Data and Machine Learning Practice Problems
Show steps
Sharpen your problem-solving skills and improve your understanding of big data and machine learning concepts through practice.
Browse courses on
Big Data
Show steps
-
Find practice problems online or in textbooks
-
Solve problems related to big data and machine learning
Build a machine learning model with Vertex AI
Show steps
Create a machine learning pipeline using Vertex AI, implementing the steps of data preparation, model training, and evaluation to reinforce your understanding of the machine learning workflow.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset
-
Prepare and preprocess the data
-
Select a machine learning algorithm
-
Train and evaluate the model
-
Deploy the model
Create a presentation on the benefits of using Google Cloud for big data and machine learning
Show steps
Research and prepare a presentation that highlights the advantages of using Google Cloud's services for big data management and machine learning, reinforcing your knowledge and communication skills.
Browse courses on
Big Data
Show steps
-
Research the benefits of using Google Cloud for big data and machine learning
-
Organize and structure your presentation
-
Create visual aids to support your points
-
Practice and deliver your presentation
Contribute to open-source projects related to big data or machine learning
Show steps
Identify open-source projects that align with your interests, participate in code reviews, contribute bug fixes, or enhance project documentation to gain hands-on experience and deepen your understanding of practical applications.
Browse courses on
Big Data
Show steps
Revisit machine learning preprocessing steps
Show steps
Reviewing machine learning preprocessing steps will refresh your knowledge and strengthen your understanding of data preparation techniques, which are crucial for successful model building.
Show steps
Show steps
Show steps
- Read through your notes or textbooks on machine learning preprocessing techniques.
- Go through online tutorials or articles that explain the different preprocessing steps in more detail.
- Complete practice exercises or quizzes on machine learning preprocessing.
Review streaming data processing
Show steps
Review the basics of streaming data processing to ensure a strong foundation for the course.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Identify different data stream characteristics
- Explore various streaming data processing tools
- Practice setting up and managing streaming data pipelines
Review notes from previous courses
Show steps
Reread course notes and review past assignments and quizzes to strengthen your understanding of key concepts in big data and machine learning.
Browse courses on
Big Data
Show steps
Show steps
Show steps
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice SQL Queries
Show steps
Reinforce your knowledge of SQL queries to prepare for working with big data in Google Cloud.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review SQL syntax and commands
- Solve practice problems using SQL
Follow Google Cloud BigQuery Tutorials
Show steps
Gain hands-on experience with Google Cloud BigQuery through interactive tutorials provided by Google.
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Complete the BigQuery Quickstart tutorial
- Explore the BigQuery documentation for advanced tutorials
Compile a glossary of big data and machine learning terms
Show steps
Create a document that defines and explains important terms used in big data and machine learning to improve your understanding and recall of key concepts.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Identify key terms from course materials
- Research and write definitions
- Organize and categorize the terms
Participate in Online Discussion Forums
Show steps
Engage with fellow learners to exchange ideas, ask questions, and clarify concepts related to big data and machine learning.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Join the course discussion forum
- Post questions and respond to your peers
Complete Google Cloud Quickstart tutorials
Show steps
Follow official Google Cloud tutorials to practice creating and querying datasets in BigQuery, gaining hands-on experience with big data tools.
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Set up a Google Cloud account
- Create a BigQuery dataset
- Import data into BigQuery
- Write SQL queries to retrieve data
Join a study group and assist other students
Show steps
Engage with fellow students by participating in a study group, offering support and clarification to enhance your own understanding while reinforcing concepts through teaching.
Show steps
Show steps
Show steps
Build a Simple Machine Learning Model with Vertex AI
Show steps
Apply your understanding of machine learning concepts by creating a simple model using Vertex AI.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset and problem to solve
- Create a model using Vertex AI
- Evaluate and deploy the model
Work Through Big Data and Machine Learning Practice Problems
Show steps
Sharpen your problem-solving skills and improve your understanding of big data and machine learning concepts through practice.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Solve problems related to big data and machine learning
Build a machine learning model with Vertex AI
Show steps
Create a machine learning pipeline using Vertex AI, implementing the steps of data preparation, model training, and evaluation to reinforce your understanding of the machine learning workflow.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset
- Prepare and preprocess the data
- Select a machine learning algorithm
- Train and evaluate the model
- Deploy the model
Create a presentation on the benefits of using Google Cloud for big data and machine learning
Show steps
Research and prepare a presentation that highlights the advantages of using Google Cloud's services for big data management and machine learning, reinforcing your knowledge and communication skills.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Research the benefits of using Google Cloud for big data and machine learning
- Organize and structure your presentation
- Create visual aids to support your points
- Practice and deliver your presentation
Contribute to open-source projects related to big data or machine learning
Show steps
Identify open-source projects that align with your interests, participate in code reviews, contribute bug fixes, or enhance project documentation to gain hands-on experience and deepen your understanding of practical applications.
Browse courses on
Big Data
Show steps
Show steps
Show steps
Career center
Learners who complete Google Cloud Big Data and Machine Learning Fundamentals will develop knowledge and skills
that may be useful to these careers:
Machine Learning Architect
Machine Learning Architect
Machine learning architects are responsible for the design, implementation, and management of machine learning systems. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as a machine learning architect. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a machine learning architect.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers are responsible for the design, implementation, and management of artificial intelligence systems. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as an artificial intelligence engineer. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as an artificial intelligence engineer.
Big Data Architect
Big Data Architect
Big data architects are responsible for the design, implementation, and management of big data systems. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as a big data architect. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a big data architect.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineering roles are highly correlated with the course, as it teaches the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud. The course could be useful in helping to prepare for a role as a machine learning engineer.
Data Scientist
Data Scientist
Data scientists are often involved in the development of machine learning models. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as a data scientist. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a data scientist.
Data Integration Engineer
Data Integration Engineer
Data integration engineers are responsible for the design, implementation, and management of data integration systems. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as a data integration engineer. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a data integration engineer.
Data Warehouse Architect
Data Warehouse Architect
Data warehouse architects are responsible for the design, implementation, and management of data warehouses. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of working as a data warehouse architect. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a data warehouse architect.
Data Governance Analyst
Data Governance Analyst
Data governance analysts are responsible for the development and implementation of data governance policies and procedures. The course can be used to develop skills in working with big data and machine learning models, both of which are increasingly important parts of data governance. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a data governance analyst.
Business Intelligence Analyst
Business Intelligence Analyst
Business intelligence analysts may use big data and machine learning models to analyze data and make recommendations. The course may help build a foundation in data engineering, data science, and machine learning that could be useful in getting a job as a business intelligence analyst.
Cloud Computing Architect
Cloud Computing Architect
Cloud computing architects are responsible for the design, implementation, and management of cloud computing systems. The course can be used to develop skills in working with big data and machine learning models, both of which are important parts of cloud computing. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a cloud computing architect.
Database Administrator
Database Administrator
Database administrators are responsible for the design, implementation, and management of databases. The course can be used to develop skills in working with big data and machine learning models, both of which are becoming increasingly important parts of database administration. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a database administrator.
Software Engineer
Software Engineer
Software engineers may find this course helpful in learning more about working with big data and machine learning models, which could be used to develop software. The course may be useful in helping to build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a software engineer.
Data Architect
Data Architect
Data architects may be involved in the design of big data and machine learning systems. The course may help build a foundation in data engineering, data science, and machine learning that could be helpful in getting a job as a data architect.
Data Analyst
Data Analyst
Data analysts can play a role in the machine learning lifecycle. The course can be used to develop skills in building a big data pipeline and machine learning models, which are both important parts of working as a data analyst. The course may help build a foundation in data engineering and machine learning that could be helpful in getting a job as a data analyst.
Data Engineer
Data Engineer
Data engineers may be involved in working with big data and machine learning, and this course may provide training in both subjects. The course may be useful in helping to build a foundation in data engineering and machine learning that could be helpful in getting a job as a data engineer.
For more career information including salaries, visit:
OpenCourser.com/course/hhmm8h/google
Reading list
We've selected nine 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
Google Cloud Big Data and Machine Learning Fundamentals.
Provides a practical guide to TensorFlow, a popular open-source machine learning library. It covers topics such as data preprocessing, model building, and model evaluation. This book great resource for anyone who wants to use TensorFlow for machine learning projects.
Provides a practical guide to using Spark for advanced analytics, covering topics such as machine learning, graph processing, and natural language processing. It great resource for anyone who wants to use Spark for advanced analytics projects.
Classic textbook on statistical learning, covering topics such as linear regression, logistic regression, and tree-based methods. It great resource for anyone who wants to learn more about statistical learning.
Provides a practical guide to machine learning for data science, covering topics such as data preprocessing, model building, and model evaluation. It great resource for anyone who wants to use machine learning for data science projects.
Comprehensive guide to Hadoop, a popular open-source big data processing framework. It covers topics such as data storage, processing, and analysis. It great resource for anyone who wants to learn more about Hadoop.
Comprehensive guide to Spark, a popular open-source big data processing framework. It covers topics such as data storage, processing, and analysis. It great resource for anyone who wants to learn more about Spark.
Provides a comprehensive overview of machine learning with big data, including topics such as data preprocessing, feature engineering, and model selection. It great resource for anyone who wants to learn more about machine learning with big data.
Save
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It great resource for anyone who wants to learn more about deep learning.
Provides a gentle introduction to machine learning, covering topics such as supervised and unsupervised learning, regression and classification, and natural language processing. It great resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hhmm8h/google
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