Official Google Cloud Certified: Professional Machine Learning Engineer Study Guide
- 6h 48m
- Mona Mona, Pratap Ramamurthy
- John Wiley & Sons (US)
- 2024
Expert, guidance for the Google Cloud Machine Learning certification exam
In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you’ll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer.
The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments.
The book also shows you how to:
- Frame ML problems and architect ML solutions from scratch
- Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools
- Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards
A can’t-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.
About the Author
MONA is an AI/ML specialist in the Google Public Sector. She is the author of Natural Language Processing with AWS AI Services and a frequent speaker at cloud computing and machine learning events. She was a Sr. AI/ML specialist SA at AWS before joining Google. She has 14 Certifications and has created courses for AWS AI/ML Certification Speciality Exam readiness. She has authored 17 articles on AI/ML and also co-authored a research paper on CORD-19 Neural Search, which won an award at the AAAI Conference on Artificial Intelligence
Pratap Ramamurthy is an AI/ML Specialist Customer Engineer in Google Cloud. Previously, he worked as a Sr. Principal Solution Architect at H2O.ai and before that was a Partner Solution Architect at AWS. He has authored several research papers and holds 3 patents.
In this Book
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Introduction
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Framing ML Problems
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Exploring Data and Building Data Pipelines
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Feature Engineering
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Choosing the Right ML Infrastructure
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Architecting ML Solutions
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Building Secure ML Pipelines
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Model Building
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Model Training and Hyperparameter Tuning
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Model Explainability on Vertex AI
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Scaling Models in Production
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Designing ML Training Pipelines
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Model Monitoring, Tracking, and Auditing Metadata
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Maintaining ML Solutions
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BigQuery ML
