Curriculum

13 Chapters · 13 Lessons

  1. 1

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  2. 2

    Chapter 1: Introduction to GCP and ML

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      Introduction to GCP and ML

  3. 3

    Chapter 2: Data Engineering and Preparation for Machine Learning

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      Data Engineering and Preparation for Machine Learning

  4. 4

    Chapter 3: Prototyping, Experimentation, and Collaboration

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      Prototyping, Experimentation, and Collaboration

  5. 5

    Chapter 4: Vertex AI Custom Model Training and Scaling

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      Vertex AI Custom Model Training and Scaling

  6. 6

    Chapter 5: Leveraging Pre-Built Models, AutoML, and Low-Code AI Solutions

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      Leveraging Pre-Built Models, AutoML, and Low-Code AI Solutions

  7. 7

    Chapter 6: Specialized Machine Learning Techniques and Responsible AI

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      Specialized Machine Learning Techniques and Responsible AI

  8. 8

    Chapter 7: Model Deployment, Serving, and Scaling

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      Model Deployment, Serving, and Scaling

  9. 9

    Chapter 8: MLOps: Automating and Orchestrating Machine Learning Pipelines

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      MLOps: Automating and Orchestrating Machine Learning Pipelines

  10. 10

    Chapter 9: Model Monitoring, Maintenance, and Governance

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      Model Monitoring, Maintenance, and Governance

  11. 11

    Chapter 10: Practice Questions and Mock Exams

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      Practice Questions and Mock Exams

  12. 12

    Chapter 11: Exam Strategies and Tips

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      Exam Strategies and Tips

  13. 13

    Index

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      Index

About the Course

The Google Cloud Professional Machine Learning Engineer certification is one of the most sought-after credentials in AI and data engineering. Ultimate Google Professional Machine Learning Engineer Exam Guide provides a structured, end-to-end preparation path from foundational GCP and ML concepts through advanced production architectures, using the Vertex AI platform as the central thread throughout. You will explore the complete machine learning lifecycle covering data ingestion, distributed training, model deployment and monitoring using Vertex AI Pipelines, BigQuery ML, and Dataflow. The book addresses fine-tuning foundation models, implementing Retrieval Augmented Generation(RAG) for generative AI applications, and scaling custom training using GPUs as well as distributed strategies, grounding every concept in industry-aligned case studies and practical implementation scenarios. The final section covers Responsible AI, including fairness, bias mitigation, model explainability, and security risks, with rigorous mock exams and proven exam strategies. Thus, by the end of the book, you will have the technical depth and practical confidence to pass the Professional ML Engineer certification and lead production AI initiatives on Google Cloud.