This is the code repository for The Definitive Guide to Google Vertex AI, published by Packt.
Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices
While AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management.
This book covers the following exciting features:
- Understand the ML lifecycle, challenges, and importance of MLOps
- Get started with ML model development quickly using Google Vertex AI
- Manage datasets, artifacts, and experiments
- Develop no-code, low-code, and custom AI solution on Google Cloud
- Implement advanced model optimization techniques and tooling
- Understand pre-built and turnkey AI solution offerings from Google
- Build and deploy custom ML models for real-world applications
- Explore the latest generative AI tools within Vertex AI
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
export PROJECT=$(gcloud config list project --format "value(core.project)")
docker build . -f Dockerfile.example -t "gcr.io/${PROJECT}/tfcustom:latest"
docker push "gcr.io/${PROJECT}/tf-custom:latest"
Following is what you need for this book: If you are a machine learning practitioner who wants to learn end-to-end ML solution development on Google Cloud Platform using MLOps best practices and tools offered by Google Vertex AI, this is the book for you. With the following software and hardware list you can run all code files present in the book (Chapter 1-17).
| Chapter | Software required | OS required |
|---|---|---|
| 1-17 | Python 3.8 or later | Any OS |
| 1-17 | Google Cloud SDK | Any OS |
| 1-17 | A Google Cloud Platform account | N/A |
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Journey to Become a Google Cloud Machine Learning Engineer [Packt] [Amazon]
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The Ultimate Guide to Building a Google Cloud Foundation [Packt] [Amazon]
Jasmeet Bhatia is a machine learning solution architect with over 18 years of industry experience, with the last 10 years focused on global-scale data analytics and machine learning solutions. In his current role at Google, he works closely with key GCP enterprise customers to provide them guidance on how to best use Google’s cutting-edge machine learning products. At Google, he has also worked as part of the Area 120 incubator on building innovative data products such as Demand Signals, and he has been involved in the launch of Google products such as Time Series Insights. Before Google, he worked in similar roles at Microsoft and Deloitte. When not immersed in technology, he loves spending time with his wife and two daughters, reading books, watching movies, and exploring the scenic trails of southern California. He holds a bachelor’s degree in electronics engineering from Jamia Millia Islamia University in India and an MBA from the University of California Los Angeles (UCLA) Anderson School of Management.
Kartik Chaudhary is an AI enthusiast, educator, and ML professional with 6+ years of industry experience. He currently works as a senior AI engineer with Google to design and architect ML solutions for Google’s strategic customers, leveraging core Google products, frameworks, and AI tools. He previously worked with UHG, as a data scientist, and helped in making the healthcare system work better for everyone. Kartik has filed nine patents at the intersection of AI and healthcare. Kartik loves sharing knowledge and runs his own blog on AI, titled Drops of AI. Away from work, he loves watching anime and movies and capturing the beauty of sunsets.