AWS Certified Machine Learning Study Guide. Shreyas Subramanian

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СКАЧАТЬ A glossary of key terms from this book is available as a fully searchable PDF.

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      Conventions Used in This Book

      This book uses certain typographic styles in order to help you quickly identify important information and to avoid confusion over the meaning of words such as on-screen prompts. In particular, look for the following styles:

       Italicized text indicates key terms that are described at length for the first time in a chapter. (Italics are also used for emphasis.)

       A monospaced font indicates the contents of configuration files, messages displayed at a text-mode Linux shell prompt, filenames, text-mode command names, and Internet URLs.

        Italicized monospaced text indicates a variable—information that differs from one system or command run to another, such as the name of a client computer or a process ID number.

        Bold monospaced text is information that you're to type into the computer, such as at a shell prompt. This text can also be italicized to indicate that you should substitute an appropriate value for your system.

      In addition to these text conventions, which can apply to individual words or entire paragraphs, a few conventions highlight segments of text:

      A note indicates information that's useful or interesting but that's somewhat peripheral to the main text. A note might be relevant to a small number of networks, for instance, or it may refer to an outdated feature.

      A tip provides information that can save you time or frustration and that may not be entirely obvious. A tip might describe how to get around a limitation or how to use a feature to perform an unusual task.

      Warnings describe potential pitfalls or dangers. If you fail to heed a warning, you may end up spending a lot of time recovering from a bug, or you may even end up restoring your entire system from scratch.

       Real World Scenario

      Real-World Scenario

      A real-world scenario is a type of sidebar that describes a task or an example that's particularly grounded in the real world. This may be a situation we or somebody we know has encountered, or it may be advice on how to work around problems that are common in real, working ML environments.

AWS Certified Machine Learning Specialty Exam Objectives

      AWS Certified Machine Learning Study Guide has been written to cover every AWS exam objective at a level appropriate to its exam weighting. The following table provides a breakdown of this book's exam coverage, showing you the weight of each section and the chapter where each objective or subobjective is covered:

Subject Area	% of Exam
Domain 1: Data Engineering Domain 2: Exploratory Data Analysis Domain 3: Modeling Domain 4: Machine Learning Implementation and Operations	20% 24% 36% 20%
Total	100%

      Domain 1: Data Engineering

      Subdomain 1.1: Create Data Repositories for Machine Learning

Exam Objective	Chapter
1.1-1. Create data repositories for machine learning	5
Identify data sources	5
Determine storage mediums	2, 5

      Subdomain 1.2: Identify and Implement a Data Ingestion Solution

Exam Objective	Chapter
1.2-1. Data job styles/types (batch load/streaming)	6, 7
1.2-2. Data ingestion pipelines	7
Kinesis	7
Kinesis Analytics	7
Kinesis Firehose	7
EMR	7
Glue	7
1.2-3. Job scheduling	7, 15

      Subdomain 1.3: Identify and Implement a Data Transformation Solution

Exam Objective	Chapter
1.3-1. Transforming data transit (ETL: Glue, EMR, AWS Batch)	6 СКАЧАТЬ В начало < 5 6 7 8 9 10 11 12 13 > В конец