AWS Machine Learning – Specialty Certification Preparation (Preview)

OverviewStepsAuthor
DifficultyAdvanced
Duration21h 18m
Students700
Ratings
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Description

******** PREVIEW *******

This Learning Path is still in development and requires additional content to cover all of the points assessed in the Exam. We are busy working on new content to fill all the gaps. Until then, the Courses and Labs currently available, in this Learning Path, will allow you to get started in preparing for this certification.

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Updates:

12th December 2019 - Added Lab: Using SageMaker Notebooks to Train and Deploy Machine Learning Models

19th June 2019 - Added Lab: Forecast Flight Delays with Amazon SageMaker

Learning Path Overview

Specifically designed to help you prepare for the AWS Machine Learning - Specialty Certification, this preview Learning Path provides interactive content comprised of hands-on labs and video Courses. This training content has been carefully created to help you study for this AWS certification.   

Learning Objectives

The aim of the certification is to validate your knowledge across a number of different key areas, which have been defined by AWS as being able to:

  • Select and justify the appropriate ML approach for a given business problem.
  • Identify appropriate AWS services to implement ML solutions.
  • Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

As a means of demonstrating this knowledge, you will be tested across 6 different domains, with each domain contributing to a total percentage of your overall score. These domains are broken down as:

  • Domain 1: Data Engineering 20% 
  • Domain 2: Exploratory Data Analysis 24% 
  • Domain 3: Modelling 36% 
  • Domain 4: Machine Learning Implementation and Operations 20%

Intended Audience 

This Learning Path is suitable for those wanting to pass the AWS Machine Learning - Specialty Certification Exam.

Prerequisites

This is one of the 4 specialty level certifications available with AWS and it's guided to those who already have experience with AWS, and ideally have already passed an Associate level Exam providing some foundation knowledge of AWS. In addition to this, it is recommended you have experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud, but these are not prerequisites in taking this certification.  

Feedback

We welcome all feedback and suggestions - please contact us at support@cloudacademy.com if you are unsure about where to start or if you would like help getting started. 

Certificate

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Learning Path Steps

1 courses

In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field.

2 courses

This course provides an in-depth introduction to the principles and practice of Amazon Machine Learning.

3 courses

This course covers Distributed Machine Learning, Apache Spark, Amazon Elastic Map Reduce, Spark MLib, and AWS Glue.

4 labs

Develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI in this Lab.

5 labs

Take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform CPU vs GPU performance analysis for AWS Machine Learning in this Lab.

6 courses

In this course, you'll learn about Amazon Rekognition, a service that enables you to easily and quickly integrate computer vision features directly into your own applications.

7 labs

Learn how to implement object detection on every new image uploaded on Amazon S3.

8 courses

In this course, you'll learn about the key features and components of Amazon Lex, and how to develop, configure, and build an end-to-end Chatbot using the Lex service.

9 labs

Join this Lab and gain experience using an MXNet convolutional neural network to style images and monitor the GPU used for training in Amazon CloudWatch.

10 labs

This lab uses Amazon SageMaker to create a machine learning model that forecasts flight delays for US domestic flights.

11 labs

In this lab, you'll use a SageMaker notebook to learn how to write Python code to prepare data, train and deploy models, and use them for real-time inference.

12 courses

This course explains AWS Identity & Access Management (IAM), what it is, and how to implement it.

13 labs

Learn how to manage our organization using IAM Users and Groups and IAM Roles 

14 exam-filled

Knowledge Check: Overview of AWS Identity and Access Management (IAM)

15 courses

In this course, you will learn the basics of KMS, what it will cost to implement, how to encrypt data, and more...

16 courses

This course covers the wide range of storage services within AWS, their key features, and when and why you would use them.

17 exam-filled

Knowledge Check: AWS Storage Fundamentals

19 exam-filled

Preview Exam: Certified Machine Learning - Specialty for AWS

About the Author

Students64568
Labs1
Courses61
Learning paths41

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data centre and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 50+ courses relating to Cloud, most within the AWS category with a heavy focus on security and compliance

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.

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