How AWS Machine Learning Helped Solidify My Career Vision and Master’s Objective
Overview
Three years into my bachelor’s in Computer Science, I felt that I had segmented, superficial knowledge in a lot of areas such as databases, networking, and programming fundamentals. However, I couldn’t visualize how it all fit together in the industry nor how to pick a more specialized field.
This short blog covers exam preparation for the AWS Machine Learning certification, why it’s worth it, and how it helped me find answers to questions I didn’t yet have.
Exam Preparation
I recommend taking the Cloud Practitioner exam first (if you haven’t taken any AWS certifications yet). It helps you understand the technical lingo/jargon that AWS throws at you and expects you to know about. I took the AWS ML exam with no prior AWS experience and only one ML class, so the cloud practitioner exam was essential. Additionally, AWS gives a 50% coupon for following tests, and since this test is much cheaper, you actually save money overall by taking this test first.
In the beginning the sheer breadth of material covered felt impossible, and I started to doubt myself on if I could really achieve this goal without the recommended level of experience suggested. What worked for me was not getting bogged down in the specifics and repeating the content iteratively. As I started putting the pieces together, I went online and further explored each area through short, topic-specific blog posts.
Depending on whether you’re an AWS expert trying to gain ML knowledge, or experienced with ML but a novice at AWS (like me), you’ll have to approach this exam very differently. Even though I had just taken the Cloud Practitioner exam, there were AWS terms I had never heard about. If you are in the other position, expect to spend a lot of time learning the fundamentals of machine learning (my dad describes this as feeling like a course with the amount of lecture material he ended up watching).
Roadmap
- ACloudGuru: I did this first because of the lecture format and guided labs to get your hands dirty and actually excited about what you are about to learn.
- Practice Tests: I bought 6+ practice exams on Udemy and took them in exam settings. Afterwards, make sure you understand not only every question but the concepts behind them as well. The more practice you do the more likely you will see every type of question beforehand.
- AWS Exam Readiness Course: This is a great comprehensive “study guide” to check your understanding before the exam. Review everything on here! It walks through the exam guide and hits every bullet point on there.
Why It’s Worth It
At face value, the AWS Machine Learning certification is well-known, respected, and a concrete indicator of your ability in both ML and cloud technologies.
AWS teaches a realistic environment with several important constraints. To name a few: cost, availability (AZs/regions), scalability (serverless), security (IAM), and usability (APIs). It changed my mindset, helping me take off my “student hat” and put on an “industry hat.”
How It Helped Me
One year ago, I had no idea what type of career I wanted to pursue beyond the vague notion that it would be technical and probably related to what I was learning in class.
Once I saw the pros and cons of working with cloud and ML as tools, the intersection of them solidified my interest in machine learning production models deployed on the cloud to deliver data driven insights.
In just a year, I went from an undergrad finding his way in a huge field of computer science to a grad student specializing in machine learning and having this goal in mind.
What’s Next: I hope to find time exploring and creating ML pipelines and working with real data at scale!
