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.
Google Cloud Big Data and Machine Learning Fundamentals
Google Cloud Big Data and Machine Learning Fundamentals
This course is part of multiple programs.
Instructor: Google Cloud Training
Sponsored by Coursera Learning Team
325,991 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(16,174 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 9 hours
Learn at your own pace
95%
Most learners liked this course
What you'll learn
Identify the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
Design streaming pipelines with Dataflow and Pub/Sub and dDesign streaming pipelines with Dataflow and Pub/Sub.
Identify different options to build machine learning solutions on Google Cloud.
Describe a machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.
Details to know
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Assessments
5 assignments
Taught in English
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There are 7 modules in this course
This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
What's included
2 videos
This section explores the key components of Google Cloud's infrastructure. It's here that we introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
What's included
10 videos1 reading1 assignment1 app item
This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Looker Studio.
What's included
9 videos1 reading1 assignment1 app item
This section introduces learners to BigQuery, Google's fully-managed, serverless data warehouse. It also explores BigQuery ML, and the processes and key commands that are used to build custom machine learning models.
What's included
9 videos1 reading1 assignment1 app item
This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.
What's included
8 videos1 reading1 assignment
This section focuses on the three key phases--data preparation, model training, and model preparation--of the machine learning workflow in Vertex AI. Learners get the opportunity to practice building a machine learning model with AutoML.
What's included
8 videos1 reading1 assignment1 app item
This section reviews the topics covered in the course, and provides additional resources for further learning.
What's included
1 video
Instructor
Instructor ratings
4.7 (2,788 ratings)Offered by
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Learner reviews
4.6
16,174 reviews
- 5 stars
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Showing 3 of 16174
AP
5
Reviewed on Nov 6, 2020
Thank you so much for gave this chance. I'm so happy with learning here. And it's more important learning this for me. Very clearly and cool course. Lerned more things. Thank you very much again.
DC
4
Reviewed on May 21, 2019
This is a very well written Introductory course on GCP products. The review tests are very good. It makes the understanding of a concept more clearly. Thank you Google for making this content.
MP
5
Reviewed on Jun 7, 2017
I highly recommend this course for an overview of what tools and services are provided by Google Cloud Platform that can be used for your web app hosting needs, big data, and machine learning.
Recommended if you're interested in Data Science
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