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AWS Certified Machine Learning Specialty — Resources and Experience
Latest   Machine Learning

AWS Certified Machine Learning Specialty — Resources and Experience

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March 4, 2022
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Last Updated on July 26, 2023 by Editorial Team

Author(s): Shravankumar Hiregoudar

Originally published on Towards AI.

This article covers my experience in getting certified with AWS Certified Machine Learning — Specialty, and I have shared the resources and cheatsheets, which helped me understand concepts! — March 2022

Source: Amazon

In the preparation phase of certification, I came across many excellent articles, blogs, and experience posts alongside the courses, which immensely helped me in understanding the width and breadth of the AWS ML world. I want to share my experience and the resources I found along the way, which boosted my confidence to take up the certification! Alright! Let's fill in some colors.

What all are we talking about in this article?

Experience
a. What's my experience with AWS and ML?
b. Why the learning and what was my approach?

Resources
a. About the exam
b. Courses
c. Practice Tests
d. Youtube & Git Resources
e. Technical Blogs / Revision Materials / Cheatsheets

1. Experience

Photo by Raimond Klavins on Unsplash

What's my experience with AWS and ML?

As of March 2022, I have about three years of experience in DATA. To dissect that a bit, I have worked as Data Scientist for 1.5 years, where I collected the data, pre-processed, built, and evaluated models for R&D use cases. I have 1.5 years of consulting experience where I worked with a multitude of platforms, helping our clients to make the best use of their data by building ETL pipelines, dashboards, cloud migration, and ML solutions.

Why the learning, and what was my approach?

I took up this learning challenge to experience the wide variety of AWS services and solve data problems. I had essential experience with AWS cloud by learning in AWS Certified Solutions Architect — Associate course. It gave me a good understanding of all the AWS services, how they communicate, and what problems they solve. This knowledge gave me a good baseline understanding which boosted my confidence to take up the ML specialty learning as I love to grow in ML.

Atomic Habits by James Clear

For me, Consistency was the key. Opening my laptop after work and actively following the instructor and labs helped me gain more knowledge day by day.

For 40 days, I was dedicated to learning after work, Learning a new concept/understanding the same concept in a more profound sense every day. For the initial 25 days, I dedicated 1–3 hrs per day depending on the workload, and for the last 15 days, I dedicated 3–5 hrs per weekday and 8–9 hrs per day on Saturdays and Sundays.

2. Resources

https://docs.aws.amazon.com/

About the exam

The exam consists of Modeling and ML implementation alongside EDA and Data Engineering. You either take the exam from home or the test center for 3 hours and answer 65 multiple correct answers for a multiple choice quiz. Based on your correct responses, they give you a score between 100 to 1000, with a passing score of 750. Fortunately, there was no negative marking; Unfortunately, there was no partial score for multiple correct answers.

Prerequisites → There are no prerequisites according to AWS; But, I would highly encourage associative level AWS learning and Machine learning by Andrew Ng course on Coursera / Youtube for a in-depth understanding of ML.

AWS Certification U+007C AWS Training & Certification

Courses

I did two courses that gave a high-level understanding of the ML capabilities of AWS.

A Cloud Guru is a Hands-on lab designed to challenge your intuition, creativity, and knowledge of the AWS platform. With this course, you'll get a solid understanding of the services and platforms available on AWS for Machine Learning projects. But, In my opinion, It fails to cover deep learning, transfer learning, and algorithms in detail which is highly important for the exam.

StephanMaarek's course explains NLP, Deep Learning, Transfer Learning, and built-in algorithms much better than ACG but doesn't cover a lot of hands-on labs.

So, Going through both the courses will draw a better picture for sure!

U+2705A Cloud Guru ($47/month)

U+2705Stephane Maarek ($15 one-time purchase)

Make a lot of written notes and mark the concepts that you didn't understand entirely so that you can revisit them (I had made a checkbox list of all challenging topics, and by the time of the exam, I had revisited them multiple times and had checked them off). Post your queries on StackOverflow and speak to people who have taken the exam to get a fresh perspective.

Start taking some practice tests once you have good exposure to Data engineering concepts, AWS services, built-in algorithms, and sagemaker!

Practice Tests

There are a lot of good practice tests out there but do not overfit (ML pun!) your brain to these tests; let your brain learn to build a better thinking approach to these rather than restricting yourself to the practice tests. Practice tests are an excellent way to know your weakness so that you can revisit those concepts on Youtube or courses or by building stuff out in sagemaker and get better at it.

U+2705awsstatic (free)

U+2705testprep

U+2705SkillCertPro

U+2705Whizlabs / Tutorials Dojo / A Cloud Guru

Note down all the unknown words, services, algorithms, concepts, approaches and revisit them on Youtube! Understand the mechanism and come back to practice tests.

Youtube & Git Resources

YT is an excellent resource for getting a deeper understanding of ML concepts and doing hands-on labs. After the courses, I was not confident in Deep learning when I targeted individual challenging concepts through YT videos, blogs, and hands-on sessions.

U+2705Fully-Managed Notebook Instances with Amazon SageMaker — a Deep Dive — AWS YT Channel; Covers in-depth sagemaker concepts

U+2705StatQuest!!! & Making Friends with Machine Learning — Best ML resources to understand the underlying concepts of ML and very well explained

U+2705Amazon SageMaker Examples — GitHub repo of the jupyter notebooks for all the built-in ML algorithms

U+2705ML YouTube Courses — List of some of the best and most recent machine learning courses available on YouTube

Now's time to go through some technical blogs written by AWS-certified folks to understand what they consider essential for the exam!

Technical Blogs / Revision Materials / Cheatsheets

These are the best resources for revising one week before the exam.

U+2705https://ffflora.cat/posts/– 8 articles which will help you to revise the entire AWS ML world

U+2705https://sudo-code7.github.io/ — Great articles customized for the exam & excellent BUILT-IN Algorithms cheetsheet!

U+2705AWS Certification — Machine Learning Concepts — Cheat Sheet

AWS Resources

U+2705https://docs.aws.amazon.com/ (Make yourself familiar with all the AI/ML services which you might have heard about in courses/practice tests; Check the list mentioned below!)

Most important ones for the ML exam (Source: https://docs.aws.amazon.com/)

U+2705Common Information About Built-in Algorithms (Refer Table: Algorithm name, Channel name, Training input mode, File type, Instance class, Parallelizable)

Persona notes; source — https://docs.aws.amazon.com/sagemaker/latest/dg/common-info-all-im-models.html

U+2705 Serverless services in AWS (Very important to know which services are serverless)

https://blogs.itemis.com/en/serverless-services-on-aws

Inputs for test-takers

Courses → Practice tests → Blogs, YT, Hand-on → practice tests → Blogs, YT, Hand-on → Cheatsheets & Revise well → AWS Exam!

Conclusion

This learning journey gave me a great understanding of machine learning and how to solve problems using AWS services. I am excited to try these out and help clients grow their businesses!

All the best for your exam! 🙂

Thank you for reading the article! Happy studying 🙂

Photo by Dayne Topkin on Unsplash

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