AWS Machine Learning – Specialty Certification Preparation

AVG Duration52h
Course Created with Sketch. 39 Exams Created with Sketch. 14 Labs Created with Sketch. 15


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 six 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.


This is one of the four 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 if you are unsure about where to start or if you would like help getting started. 


Your certificate for this learning path

Learning Path Steps


This course introduces the AWS Certified Machine Learning - Specialty learning path which prepares you to take the certification exam.


In this course, follow along with AWS certification specialist, Stephen Cole, as he discusses his experience taking the AWS Machine Learning - Specialty Exam.


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


This course has been expertly created to provide you with a strong foundation in machine learning and deep learning.


This course is the first in a two-part series covering the fundamentals of machine learning.


This course is part two of the module on machine learning and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.


Knowledge Check: Practical Machine Learning - Module 0


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


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.


This course covers the basics of python in machine learning, how to use loops, regressions, and classification, and how to set up machine learning in python.


Knowledge Check: Practical Machine Learning - Module 1


Learn the ways in which data comes in many forms and formats with this course.


This course covers the foundations and history of machine learning as well as the principles of memory storage, computing power, and phone/web applications.


Knowledge Check: Zero to Deep Learning - Introduction to Data Science and Machine Learning


This course is the first part of the two-part series on the mathematics of machine learning.


This course is the second part of the two-part series on the mathematics of machine learning.


Knowledge Check: Practical Machine Learning - Module 2


This course gives an informative introduction to deep learning and introducing neural networks.


This course expertly covers the essentials needed to succeed in machine learning.


Knowledge Check: Zero to Deep Learning - Introduction to Deep Learning


In this course, discover convolutions and the convolutional neural networks involved in Data and Machine Learning.


In this course, you'll learn how to use recurrent neural networks to train more complex models.


In this course, you'll learn how to improve the performance of your neural networks with this learning path.


Knowledge Check: Zero to Deep Learning - Working with Convolutional and Recurrent Neural Networks


This course provides a practical understanding of the steps required to build and deploy machine learning models using Amazon SageMaker.


Get started with the latest SageMaker Data Wrangler, Data Pipeline and Feature Store services (released at re:invent Dec 2020) and SageMaker Ground Truth


The course introduces you to supervised learning and the nearest neighbors algorithm.


This course explores hyperparameters, distance functions, similarity measures, logistic regression, the method and workflow of machine learning and evaluation, and the train-test split.


Knowledge Check: Practical Machine Learning - Module 3


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.


This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you.


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


Knowledge Check: Start Modeling Data with Amazon SageMaker


Selecting the right machine learning model will help you find success in your projects. In this module, we’ll discuss how to do so, as well the difference between explanatory and associative approaches, before we end on how to use out-sample performance.


Knowledge Check: Practical Machine Learning - Module 4


This course explores the core concepts of machine learning, the models available, and how to train them.


This lab is aimed at machine learning beginners who want to understand how to train custom models.


This lab will walk you through building several binary classification models using different model methodologies and then comparing the model predictions using evaluation tools.


How do you know that your models will do a good job making predictions on new, unseen data? This lab will discuss the fundamentals.


Knowledge Check: Getting Started with Machine Learning Models


Regression is a widely used machine learning and statistical tool and it’s important you know how to use it. In this module, we’ll discuss interpreting modes, as well as how to interpret linear classification models.


This lab walks you through building several multivariate linear regression models using different prediction variables and evaluating the models' predictions.


Knowledge Check: Practical Machine Learning - Module 5


This course covers the concept of unsupervised learning within the context of machine learning and how unsupervised learning differs from supervised learning.


This course explores the topic of probability and statistics, including various mathematical approaches and some different interpretations of probability.


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.


This lab will walk you through a number of ways to handle missing data including using a default value and building a model to predict the missing data.


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


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.


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.


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


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


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


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


In this course, you'll learn to recognize and explain what encryption is at a high level as well as the various encryption options provided by AWS.


This course will look at some of the management and bucket property features that Amazon S3 has to offer, and how you can use them to maintain and control your data.


This course explores two different Amazon S3 features: the replication of data between buckets and bucket key encryption when working with SSE-KMS to protect your data.


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


Knowledge Check: AWS Storage Fundamentals


This course introduces AWS Step Functions and its uses, benefits, and limitations.


This course explores the AWS Athena service, reviewing fundamental AWS Athena storage and querying concepts.


Use Amazon Athena to query encrypted data on S3 and encrypt the query results as well.


This course will take you through the fundamentals of AWS Glue to get you started with the service.


In this introductory course, you will learn to recognize and explain the core components of Amazon Kinesis and where those services can be applied.


In this course, you'll learn about the key features and core components of Kinesis Analytics, and what an end-to-end real-time data streaming example looks like.


Preview Exam: Certified Machine Learning - Specialty for AWS

About the Author
Learning paths3

Stephen is the AWS Certification Specialist at Cloud Academy. His content focuses heavily on topics related to certification on Amazon Web Services technologies. He loves teaching and believes that there are no shortcuts to certification but it is possible to find the right path and course of study.

Stephen has worked in IT for over 25 years in roles ranging from tech support to systems engineering. At one point, he taught computer network technology at a community college in Washington state.

Before coming to Cloud Academy, Stephen worked as a trainer and curriculum developer at AWS and brings a wealth of knowledge and experience in cloud technologies.

In his spare time, Stephen enjoys reading, sudoku, gaming, and modern square dancing.