AWS Certified Machine Learning - Specialty

AWS Certified Machine Learning - Specialty

by Milecia McGregor
Released April 2024
Publisher(s): Pearson
ISBN: 0138283133

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Video description

4+ Hours of Video Instruction

Learn the techniques and approaches to successfully pass the AWS Certified Machine Learning - Specialty Exam with hands-on exercises.

Getting the AWS Certified Machine Learning - certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know can use cloud tools, it makes you an even more valuable as an MLOps engineer. You’ll be able to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed. This certification shows that you know how to do full-stack ML development.

In this series of videos, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples in Visual Studio with Python. It’s just what you need to learn to pass the exam. It includes an overview of concepts with hands-on work using AWS tools like Kinesis and EMR.

About the Instructor:

Milecia McGregor is a software generalist who has worked in numerous areas of tech over the past decade. With a master’s degree in mechanical and aerospace engineering, Milecia has accomplished many groundbreaking projects over the years, including Machine Learning (ML) work for human-computer interfaces on autonomous vehicles; front-end and back-end; data science; robotics; DevOps; cybersecurity; VR; and more. Milecia is also an international speaker in the tech community, with talks covering a variety of topics across multiple programming languages.

Skill Level:

  • Intermediate

Learn How To:

  • Learn effective tips and techniques for passing the AWS Certified Machine Learning - Specialty exam
  • Identify and implement data ingestion solutions with Kinesis
  • Evaluate ML models
  • Deploy ML models with AWS tools

Course requirement:

  • Prerequisites: Knowledge of how to use various AWS tools to deploy ML models into different environments
  • Knowledge of.data engineering principles and model training and evaluatio

Who Should Take This Course:

Job titles: ML engineer, DevOps engineer, Aspiring ML engineer

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Table of contents

  1. Introduction
    1. AWS Certified Machine Learning – Specialty: Introduction
  2. Lesson 1: Data Engineering
    1. Learning objectives
    2. 1.1 Create data repositories for machine learning
    3. 1.2 Identify and implement a data ingestion solution
    4. 1.3 Decide between ingestion tools
    5. 1.4 Identify and implement a data transformation solution
    6. 1.5 Get some practice: questions and exercises
  3. Lesson 2: Exploratory Data Analysis
    1. Learning objectives
    2. 2.1 Sanitize and prepare data for modeling
    3. 2.2 Perform feature engineering
    4. 2.3 Analyze data for machine learning
    5. 2.4 Visualize data for machine learning
    6. 2.5 Get some practice: questions and exercises
  4. Lesson 3: Training Models
    1. Learning objectives
    2. 3.1 Frame business problems as machine learning problems
    3. 3.2 Select the appropriate model for a machine learning problem
    4. 3.3 Understand the intuition behind the model
    5. 3.4 Train machine learning models
    6. 3.5 Choose compute option
    7. 3.6 Get some practice: questions and exercises
  5. Lesson 4: Evaluating Models
    1. Learning objectives
    2. 4.1 Perform hyperparameter optimization
    3. 4.2 Use other methods for hyperparameter optimization
    4. 4.3 Evaluate machine learning models
    5. 4.4 Compare models with different metrics
    6. 4.5 Implement machine learning best practices
    7. 4.6 Get some practice: questions and exercises
  6. Lesson 5: Machine Learning Implementation and Operations
    1. Learning objectives
    2. 5.1 Build machine learning solutions for production
    3. 5.2 Address scaling concerns
    4. 5.3 Recommend and implement the appropriate machine learning services
    5. 5.4 Apply basic AWS security practices to machine learning solutions
    6. 5.5 Deploy and operationalize machine learning solutions
    7. 5.6 Get some practice: questions and exercises
  7. Summary
    1. AWS Certified Machine Learning – Specialty: Summary

Product information

  • Title: AWS Certified Machine Learning - Specialty
  • Author(s): Milecia McGregor
  • Release date: April 2024
  • Publisher(s): Pearson
  • ISBN: 0138283133