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SKU/Artículo: AMZ-B0FJ719WQV
Databricks Certified Machine Learning Associate Certification Practice 300 Questions & Answer: Includes Comprehensive Answer Explanations and Conceptual Insights
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Kindle
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Fuera de stock
Fuera de stock
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0.36 kg
0.36 kg
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- This book serves as a comprehensive guide for individuals preparing for the Databricks Certified Machine Learning Associate certification exam. It is meticulously designed to cover the entire scope of the examination, which assesses an individual's proficiency in leveraging Databricks for fundamental machine learning tasks. The certification validates the ability to understand and effectively utilize Databricks' machine learning capabilities, including advanced features like AutoML, Unity Catalog, and select functionalities of MLflow. Furthermore, it evaluates skills in data exploration, feature engineering, model building (encompassing training, tuning, and evaluation), model selection, and the crucial aspect of deploying machine learning models. Passing this certification signifies an individual's capability to execute basic machine learning tasks proficiently using Databricks and its integrated toolset.The examination's content is structured across key domains, with specific weightages:Databricks Machine Learning: 38%ML Workflows: 19%Model Development: 31%Model Deployment: 12%A detailed breakdown of the exam outline, which this book thoroughly addresses, includes:Section 1: Databricks Machine Learning This section delves into the core aspects of MLOps strategies, emphasizing best practices and the advantages of using ML runtimes. It covers how AutoML facilitates model and feature selection, highlighting its benefits in the model development process. A significant focus is placed on Unity Catalog, including the advantages of creating account-level feature store tables versus workspace-level, the practical steps to create and write data to a feature store table, and how to train and score models using features from these tables. The differences between online and offline feature tables are also explored. MLflow's role is extensively covered.Section 2: Data Processing This part of the book focuses on essential data manipulation and preparation techniques within a Spark environment. It covers computing summary statistics on a Spark DataFrame using.summary()ordbutilsdata summaries, and methods for outlier removal based on standard deviation or IQR. Emphasis is placed on creating visualizations for both categorical and continuous features, and comparing feature types using appropriate methods. The book provides a comprehensive understanding of imputing missing values with mode, mean, or median, and the practical application of one-hot encoding for categorical features, including identifying appropriate scenarios for its use. It also discusses the relevance and application of log scale transformation.Section 3: Model Development This section guides the reader through the intricacies of model building. It covers selecting appropriate algorithms based on ML foundations for given scenarios and methods to mitigate data imbalance in training data. The book differentiates between estimators and transformers and provides guidance on developing robust training pipelines. Hyperparameter tuning is a key focus, detailing the use of Hyperopt'sfminoperation, and exploring random, grid, or Bayesian search methods. It also addresses parallelizing single-node models for hyperparameter tuning. Section 4: Model Deployment The final section of the book is dedicated to deploying machine learning models. It differentiates between and highlights the advantages of various model serving approaches: batch, real-time, and streaming. Practical steps for deploying a custom model to a model endpoint are provided. The book covers using pandas for performing batch inference and explains how streaming inference is achieved with Delta Live Tables.
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