What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of multiple programs.
Instructor: Google Cloud Training
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(16,174 reviews)
(16,174 reviews)
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.
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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 section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
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.
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.
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.
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.
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.
8 videos1 reading1 assignment1 app item
This section reviews the topics covered in the course, and provides additional resources for further learning.
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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Reviewed on Nov 5, 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.
Reviewed on Jun 6, 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.
Reviewed on Mar 2, 2019
Overall a good curated course to help understand the GCP offerings and high level architecture of how their offerings fit in the current landscape. Easy to follow along as this was fundamental course.
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Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:
• A common query language such as SQL
• Extract, transform, load activities
• Data modeling
• Machine learning and/or statistics
• Programming in Python
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Yes, this online course is based on the instructor-led training formerly known as CPB100.
The course covers the topics presented on the certification exam, however we recommend additional preparation including hands-on product experience. The best preparation for certification is real-world, hands-on experience. Review the Google Certified Professional Data Engineer certification preparation guide for further information and resources at https://cloud.google.com/certification/guides/data-engineer/
Google’s Certification Program gives customers and partners a way to demonstrate their technical skills in a particular job-role and technology. Individuals are assessed using a variety of rigorously developed industry-standard methods to determine whether they meet Google’s proficiency standards. Read more at https://cloud.google.com/certification/
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
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