******** 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.
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.
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: Explatory Data Analysis 24%
- Domain 3: Modelling 36%
- Domain 4: Machine Learning Implementation and Operations 20%
This Learning Path is suitable for those wanting to pass the AWS Machine Learning - Specialty Certification Exam.
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.
We welcome all feedback and suggestions - please contact us at email@example.com if you are unsure about where to start or if you would like help getting started.
Learning Path Steps
Introduction to the Principles and Practice of Amazon Machine Learning
Develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI in this Lab.
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.
Learn how to implement object detection on every new image uploaded on Amazon S3.
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.
Learn how to manage our organization using IAM Users and Groups and IAM Roles
Knowledge Check: Overview of AWS Identity and Access Management (IAM)
Knowledge Check: AWS Storage Fundamentals
Preview Exam: Certified Machine Learning - Specialty for AWS
About the Author
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.