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.
Offered By
About this Course
What you will learn
Recognize the data-to-AI lifecycle on Google Cloud and the major big data and machine learning products.
Analyze big data at scale with BigQuery.
Identify different options to build machine learning solutions on Google Cloud.
Describe a machine learning workflow and the key steps with Vertex AI.
Skills you will gain
- Tensorflow
- Bigquery
- Google Cloud Platform
- Cloud Computing
Offered by

Google Cloud
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.
Syllabus - What you will learn from this course
Course Introduction
This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
Big Data and Machine Learning on Google Cloud
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.
Data Engineering for Streaming Data
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 Data Studio.
Big Data with BigQuery
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.
Machine Learning Options on Google Cloud
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.
The Machine Learning Workflow with Vertex AI
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.
Course Summary
This section reviews the topics covered in the course, and provides additional resources for further learning.
Reviews
- 5 stars71.68%
- 4 stars23.66%
- 3 stars3.54%
- 2 stars0.53%
- 1 star0.58%
TOP REVIEWS FROM GOOGLE CLOUD BIG DATA AND MACHINE LEARNING FUNDAMENTALS
It was very good training with some of the real-time use cases enjoyed a lot. As a new person to google cloud and big data, I think this the best basic fundamental which I have come across so far.
It was great learning about Google Cloud and hands on with the various services on the platform. There are immense possibilities of Cloud technologies in the future and happy to be a part of it
Excellent overview of big data and machine learning using GCP, but would have been better if there had been notes similar to the "Google Cloud Platform Fundamentals - Core Infrastructure" course.
This course really helped me in understanding exactly 'How the Big data and Machine learning can be used in Cloud' and 'The ease to use it'. Thank you for summing all the fundamentals in this course.
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