May 1, 2024
4 minute read
Vertex AI Workbench is a cloud-based environment that provides a unified workspace for data scientists and machine learning engineers to collaborate on building, training, and deploying models. It brings together several tools and services from Google Cloud including BigQuery, Cloud Storage, and Cloud Machine Learning Engine into one integrated platform. Vertex AI Workbench makes it easy for teams to work together on machine learning projects, share data and models, and manage experiments and pipelines.
Why Learn Vertex AI Workbench?
There are many reasons to learn Vertex AI Workbench:
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Find a path to becoming a Vertex AI Workbench. Learn more at:
OpenCourser.com/topic/857w0g/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
Vertex AI Workbench.
Provides a comprehensive guide to using Google Cloud for artificial intelligence. It covers a wide range of topics, including machine learning, natural language processing, and computer vision.
Provides a comprehensive guide to using Google Cloud Platform. It covers a wide range of topics, including compute, storage, networking, and databases.
Provides a comprehensive introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive introduction to data science. It covers a wide range of topics, including data analysis, data visualization, and machine learning.
Provides a comprehensive guide to using PyTorch for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive introduction to Python for data science. It covers a wide range of topics, including data analysis, data visualization, and machine learning.
Provides a comprehensive guide to using TensorFlow for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive guide to using Go for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive introduction to R for data science. It covers a wide range of topics, including data analysis, data visualization, and machine learning.
Provides a comprehensive guide to using JAX for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive guide to using Rust for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/857w0g/vertex
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