AWS Certified Machine Learning – Specialty (MLS-C01)

Paths

AWS Certified Machine Learning – Specialty (MLS-C01)

Authors: Amber Israelsen, Kim Schmidt, Mohammed Osman, Saravanan Dhandapani, David Tucker

The AWS Certified Machine Learning Specialty certification is for developers and data scientists who want to validate their machine learning skills on the AWS platform.... Read more

The courses in this path will teach you how to frame a business problem as a machine learning problem, and then how to design, build, deploy and maintain machine learning solutions on the AWS platform. These topics are designed to prepare you for the AWS Certified Machine Learning Specialty certification exam.

Pre-requisites

This path is intended for advanced AWS practitioners with one to two years of hands-on experience architecting, developing, or running machine learning workloads in AWS. In addition, the AWS Certified Solutions Architect – Associate certification, or the AWS Certified Big Data Specialty certification are recommended before attempting the AWS Certified Machine Learning – Specialty certification.

Path Courses

The courses in this path will teach you how to frame a business problem as a machine learning problem, and then how to design, build, deploy and maintain machine learning solutions on the AWS platform. These topics are designed to prepare you for the AWS Certified Machine Learning Specialty certification exam.

Fundamentals of Machine Learning on AWS

by Amber Israelsen

Jun 2, 2020 / 2h 18m

2h 18m

Start Course
Description

You’ve probably heard about how machine learning is shaping our world--from facial recognition to package delivery, speech recognition to self-driving cars. But how do you get started in this exciting field?

In this course, Fundamentals of Machine Learning on AWS, you’ll learn how to solve business problems with AWS machine learning technologies. First, you’ll explore what ML is and how it relates to artificial intelligence and deep learning. Next, you’ll learn how to identify and frame opportunities for machine learning. Then, you’ll discover the end-to-end machine learning process: fetching, cleaning and preparing data, training and evaluating models, and deploying and monitoring models. Finally, you’ll learn the AWS artificial intelligence and machine learning technologies that enable this process, and see them in action with Amazon SageMaker Studio.

When you’re finished with this course, you’ll have the skills and knowledge of AWS machine learning technologies needed to solve real-world problems. This course will also lay the foundation for the AWS Machine Learning Specialty certification.

Table of contents
  1. Course Overview
  2. Course Introduction
  3. Identifying Opportunities for Machine Learning
  4. Defining Machine Learning Problems
  5. Fetching and Preparing Data
  6. Training and Evaluating the Model
  7. Deploying and Monitoring the Model
  8. The AWS Machine Learning Stack
  9. Next Steps

Data Engineering with AWS Machine Learning

by Kim Schmidt

Jun 18, 2020 / 2h 54m

2h 54m

Start Course
Description

Storing data for machine learning is challenging due to the varying formats and characteristics of data. Raw ingested data must first be transformed into the format necessary for downstream machine learning consumption, and once the data is ready to be used, it must be ingested from storage to the machine learning service. In this course, Data Engineering with AWS Machine Learning, you’ll learn to choose the right AWS service for each of these data-related machine learning ML tasks for any given scenario. First, you’ll explore the wide variety of data storage solutions available on AWS and what each type of storage is used for. Next, you’ll discover the differing AWS services used to ingest data into ML-specific services and when to use each one. Finally, you’ll learn how to transform your raw data into the proper formats used by the various AWS ML services. When you’re finished with this course, you’ll have the skills and knowledge of how to properly provide data solutions for storing, preparing, and ingesting data needed to architect data engineering solutions on AWS for Machine Learning, and be prepared to take the AWS Machine Learning Certification exam.

Table of contents
  1. Course Overview
  2. Important Data Characteristics to Consider in a Machine Learning Solution
  3. Typical Data Flow for Machine Learning on AWS
  4. Data Storage Options for Machine Learning on AWS
  5. Database Options for Machine Learning on AWS
  6. Using a Data Warehouse or a Data Lake as a Machine Learning Repository
  7. Streaming Data Ingestion Solutions on AWS for Machine Learning
  8. Batch Data Ingestion Solutions on AWS for Machine Learning
  9. Data Transformation Overview on AWS for Machine Learning
  10. Data-driven Workflows: The AWS Data Pipeline
  11. Data Transformation Using Apache Spark on Amazon EMR
  12. Data Transformation Using Serverless AWS Glue and Serverless Amazon Athena

Exploratory Data Analysis with AWS Machine Learning

by Mohammed Osman

May 8, 2020 / 2h 16m

2h 16m

Start Course
Description

Understanding underlying trends and outliers in data is a necessary step to do proper data preparation and feature engineering for subsequent machine learning tasks.

In this course, Exploratory Data Analysis with AWS Machine Learning, you’ll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps.

What you will learn in this beginner level AWS machine learning tutorial:

  • First, you’ll explore how to understand data trends and distribution using basic statistics.
  • Next, you’ll discover how to visualize your dataset to understand the overall patterns.
  • Finally, you’ll learn how to prepare your data for the machine learning pipeline by doing preprocessing and feature engineering.

When you’re finished with this course, you’ll have the skills and knowledge of exploratory data analysis needed to achieve AWS Machine Learning specialty certification.

Table of contents
  1. Course Overview
  2. Machine Learning with AWS
  3. Data Analysis Using AWS
  4. Data Visualization Using AWS
  5. Data Preparation Using AWS

Modeling with AWS Machine Learning

by Saravanan Dhandapani

Apr 25, 2020 / 2h 12m

2h 12m

Start Course
Description

Being the front runner when it comes to cloud infrastructure, AWS has cutting edge services when it comes to machine learning. In this course, Modeling with AWS Machine Learning, you’ll learn to convert your data to an optimal model leveraging AWS SageMaker. First, you’ll explore supervised and unsupervised learning algorithms that are built-in to your AWS account and learn how to apply them to a specific business problem. Next, you’ll discover deep learning neural networks architecture and the built-in algorithms provided by AWS that cater specifically to computer vision and language processing domain. Finally, you’ll learn how to train a model on a SageMaker notebook, evaluate the model against the objective metric, and fine-tune the hyperparameters and arrive at an optimally performing model. When you’re finished with this course, you’ll have the skills and knowledge of all the AWS built-in algorithms and train, evaluate, and tune your models that are needed to master AWS SageMaker and clear AWS Machine Learning Specialty certification exam.

Table of contents
  1. Course Overview
  2. ML Foundation and Supervised Learning Algorithms
  3. Deep Learning Foundation and Algorithms
  4. Train ML Models
  5. Evaluate ML Models
  6. Tune ML Models

Implementing and Operating AWS Machine Learning Solutions

by David Tucker

Jun 1, 2020 / 1h 56m

1h 56m

Start Course
Description

Machine Learning Implementation and Operations is one of the four domains covered by the AWS Machine Learning Specialty certification exam. In this course, Implementing and Operating AWS Machine Learning Solutions, you’ll learn key areas from this domain that are covered in the exam. First, you’ll explore the different AWS services that can support a machine learning solution in production. Next, you’ll discover how to deploy and scale a machine learning model with Amazon Sagemaker. Finally, you’ll learn how to implement security best practices for your machine learning solution with AWS. When you’re finished with this course, you’ll have the skills and knowledge in this domain needed to prepare for the AWS Machine Learning Specialty certification exam.

Table of contents
  1. Course Overview
  2. AWS Machine Learning Services
  3. Deploying a SageMaker Model
  4. Securing a SageMaker Implementation
  5. Implementing a Highly-available Machine Learning Solution

Demystifying the AWS Certified Machine Learning Specialty Exam

by David Tucker

Jun 29, 2020 / 1h 26m

1h 26m

Start Course
Description

The AWS Machine Learning Specialty Certification represents an advanced knowledge of implementing machine learning solutions on the platform. In this course, Demystifying the AWS Certified Machine Learning Specialty Exam, you’ll learn how to combine the information gained in the other courses in this path to prepare to add this certification to your resume. First, you’ll explore the exam and understand how it is structured. Second, you’ll review the four domains covered by the exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Finally, you’ll discover the best manner to leverage the study materials and prepare for the exam. When you’re finished with this course, you’ll have the techniques and resources needed to prepare to take the AWS Machine Learning Specialty certification exam.

Table of contents
  1. Course Overview
  2. Understanding the Exam
  3. Data Engineering Review
  4. Exploratory Data Analysis Review
  5. Data Modeling Review
  6. Machine Learning Operations Review