| Description |
1 online resource |
| Series |
Sybex Study Guide Series |
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Sybex study guide series.
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| Summary |
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. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Cover Page -- Title Page -- Copyright Page -- Acknowledgments -- About the Authors -- About the Technical Editors -- Contents at a Glance -- Contents -- Introduction -- Google Cloud Professional Machine Learning Engineer Certification -- Why Become Professional ML Engineer (PMLE) Certified? -- How to Become Certified -- Who Should Buy This Book -- How This Book Is Organized -- Chapter Features -- Bonus Digital Contents -- Conventions Used in This Book -- Google Cloud Professional ML Engineer Objective Map -- How to Contact the Publisher -- Chapter 1 Framing ML Problems |
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Translating Business Use Cases -- Machine Learning Approaches -- Supervised, Unsupervised, and Semi-supervised Learning -- Classification, Regression, Forecasting, and Clustering -- ML Success Metrics -- Regression -- Responsible AI Practices -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Exploring Data and Building Data Pipelines -- Visualization -- Box Plot -- Line Plot -- Bar Plot -- Scatterplot -- Statistics Fundamentals -- Mean -- Median -- Mode -- Outlier Detection -- Standard Deviation -- Correlation -- Data Quality and Reliability -- Data Skew -- Data Cleaning -- Scaling |
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Log Scaling -- Z-score -- Clipping -- Handling Outliers -- Establishing Data Constraints -- Exploration and Validation at Big-Data Scale -- Running TFDV on Google Cloud Platform -- Organizing and Optimizing Training Datasets -- Imbalanced Data -- Data Splitting -- Data Splitting Strategy for Online Systems -- Handling Missing Data -- Data Leakage -- Summary -- Exam Essentials -- Review Questions -- Chapter 3 Feature Engineering -- Consistent Data Preprocessing -- Encoding Structured Data Types -- Mapping Numeric Values -- Mapping Categorical Values -- Feature Selection -- Class Imbalance |
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Classification Threshold with Precision and Recall -- Area under the Curve (AUC) -- Feature Crosses -- TensorFlow Transform -- TensorFlow Data API (tf.data) -- TensorFlow Transform -- GCP Data and ETL Tools -- Summary -- Exam Essentials -- Review Questions -- Chapter 4 Choosing the Right ML Infrastructure -- Pretrained vs. AutoML vs. Custom Models -- Pretrained Models -- Vision AI -- Video AI -- Natural Language AI -- Translation AI -- Speech-to-Text -- Text-to-Speech -- AutoML -- AutoML for Tables or Structured Data -- AutoML for Images and Video -- AutoML for Text |
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Recommendations AI/Retail AI -- Document AI -- Dialogflow and Contact Center AI -- Custom Training -- How a CPU Works -- GPU -- TPU -- Provisioning for Predictions -- Scaling Behavior -- Finding the Ideal Machine Type -- Edge TPU -- Deploy to Android or iOS Device -- Summary -- Exam Essentials -- Review Questions -- Chapter 5 Architecting ML Solutions -- Designing Reliable, Scalable, and Highly Available ML Solutions -- Choosing an Appropriate ML Service -- Data Collection and Data Management -- Google Cloud Storage (GCS) -- BigQuery -- Vertex AI Managed Datasets -- Vertex AI Feature Store |
| Subject |
Cloud computing -- Examination -- Study guides.
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Cloud computing -- Management -- Study guides.
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Electronic data processing personnel -- Certification -- Examinations -- Study guides.
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Infonuagique -- Examen -- Guides de l'étudiant. |
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Infonuagique -- Gestion -- Guides de l'étudiant. |
| Added Author |
Ramamurthy, Pratap, author.
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| Other Form: |
Print version: Mona, Mona Official Google Cloud Certified Professional Machine Learning Engineer Study Guide Newark : John Wiley & Sons, Incorporated,c2023 9781119944461 |
| ISBN |
9781119981848 (e-book) |
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1119981840 |
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1119981565 electronic book |
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9781119981565 electronic book |
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