ydavidchen/gcp-mle-outline

Cheatsheet for studying/review the Professional Machine Learning Engineer Certification exam topics

Review Notes for GCP's Professional Machine Learning Engineer Certification Exam Topics

Overview

Welcome to the cheatsheet for studying/review the Professional Machine Learning Engineer Certification exam topics! In this repository, you will find a two-page LaTex-written cheatsheet with topics related to the exam (still in Beta mode at the time of the initial release of this repo).

The cheatsheet was initially intended for those who have:

• Taken college courses, online courses/MOOCs in data science & machine learning
• Had hands-on experience with machine learning algorithms running on your local computers, such as scikit-learning and Keras
• Had little experience with production-level ML (e.g. in GCP, AWS)

For this reason, I have re-structured the cheatsheet into the following themes (sections):

1. Preparation for ML
2. ML Model Development
3. ML in Production

In particular, Section 2 should be familiar to most of the target audience, and thus it only includes only the relevant topics but not details.

I highly recommend referencing the scikit-learn User Guide and Keras Developer Guides as needed.

Releases

The repository was initially released on 07-16-2020. It will likely be continuously updated, maintained, and debugged. If you are serious about getting well-prepared for this particular exam, (or even feeling a bit bored when staying at home alone during covid-19), consider

• Clone or fork this repository
• Add or expand sections that you believe important (or areas that you don't feel confident, wanting to learn more, etc.)
• Make changes or updates (e.g. places where you found typo or error)
• Or even better, make your own cheatsheet from scratch. Believe me, it isn't about what's on paper/screen but rather the process of making the cheatsheet (doing the research, thinking about the delivery...) that really helped

The template of the cheatsheet is freely available on Overleaf with my customizations.

Endnotes

References & Works Cited

• The main guide that led to this cheatsheet is the publicly available official exam guide from the exam provider
• Google Cloud Training's Coursera courses:
  • Advanced Machine Learning Specialization
  • Data Engineering Professional Certificate
  • Architecting with Kubernetes Specialization
• GCP solutions website. I found the "Articles" section was most helpful.
• Additional references include numerous online posts/articles/blogs (e.g. from Medium.com, StackExchange Network, Wikepedia) and open-source documentations (e.g. scikit-learn documentation, various GCP services/tools documentation, articles on Google Cloud website). References are:
  • Linked as hyperlinks (see source files)
  • Comments (tagged %) in the main.tex source code

Important Disclaimer

Please note, this cheatsheet is a review of topics publicly available with description, and does NOT provide any actual information about the exam itself. The materials here are for the sole purpose of self-education, and will always be free & open-source. There is no guarantee that the information presented is accurate or up-to-date. Use at your own discretion. Additionally, this repository does not represent views from the exam provider or any of my affiliations ( employer, academic institutions, groups, etc.). Responsibly use resources presented, especially the external resources linked/cited.

Copyright © 2020 | David Chen, PhD