Pass Google Cloud Machine Learning Exam, with this How-To Human Learning Guide
From novice to expert: Explore a cloud engineer’s roadmap to smash the Google Cloud Machine Learning Exam, today!
🚀😎 I Just Passed My ML Certification. The“Get ’em all in 2024” is on track.
It’s some seriously exciting time. It’s been the biggest exam for me to undertake, and it’s been on my radar for a long while. Overdue in fact. Not that I’ve been running many Data/ML projects in the past. And Now It’s time to collect and organise my notes and share this Journey with the Google Cloud & Machine Learning/AI communities alike, to help you get there as well. Buckle up. There is a lot to cover, and I’m not intending to hold back as “another generic exam prep guide” that is as useful as a paper umbrella.😑
Preamble
I have been operating in the Google Cloud ecosystem as a DevOps Engineer, then Consulting Principal lead and a Practice lead for a little while. Despite personally being involved on a number of GCP (non-GCP) project alike, I appreciate what cloud engineer development journey towards this ML Certification may be like, — stumbling across and considering the effort involved is vast and varied, — so here is my journey for full context. Here you would see where how I found it to be, given my own experience and context, and reflect on how you may find it relevant on your very own #LearningEveryDay journey.
My Journey towards Generative AI & Machine Learning
In that order. 🚀
You see, as a Principal Consultant, Practice lead, Technically speaking the Application Modernisation, Platforms,Security is my jam, — and ML/AI entire area was net-new for me back in 2023.
I may have been late to the generative AI journey, picking up on the wild hype around OpenAI GPT release 3 months after it’ launched in Nov-Dec of 2022. But this was the turning point for me come Q1 2023.
The proverbial “Jeanie is Out of the bottle”, — as the root inspiration for me. GenAI Changed everything, and (Data)ML is at the centre of all this.
The Rest is history, with hands-on #GenAI experimentation, kick starting and upskilling for Generative AI Consulting Projects, Speaking & Presenting on this topic, and resulting in Thought Leadership published pieces, such as below.
- Embracing Generative AI: A Technology Consultant’s Perspective on the Exponential Age
- Need for Frequent Upskill in the Age of the AI
The Generative AI is eating software that is eating the world, and my experimentation, with professional interest, highlighted the fact that business concepts and business development ideas, developer value generation was changing rapidly, and notably yet — accelerating in adoption and disruption, as a matter of fact.
Welcome to #MachineAge #ExponentialAge of disruption and Opportunities alike. This accelerated value creation paved way to my published creations such such as this PromptKeeper Chrome Extension, made with, and for #Generative AI use cases;
Why does all this matter (and Why am I taking sweet-time with this preamble section?!)
In short, this is to emphasis my own personal angle, and how my professional journey and indeed interest evolved from DevOps, Cloud Engineering, Enterprise Platforms to now Machine Learning Ecosystems and Practical implementation and value creation with ML and Generative AI — the latter being related but not the same as ML, to be pedantic.
Now that there has been an explosion of AI and ML leadership role titles of late. And as long overdue, it was time to eat my own Thought Leadership dogfood, — validate my know-how head-on, — and Lead by Example.😎
Human Learning for 🤖 Machine Learning
It’s Time. #PersonalDevelopment and all this #LearningEveryDay is intense. It’s hard. Joggling Family, Professional Projects, any resemblance of Hobbies and now this recommended 2 months+ training course is not easy.
Seriously. 2 Months (see recommedned time on this course alone). 😑
Well, I better get started then…
This is is where The Below walk-through ML Exam prep notes — I do hope help you on your very own development journey with Google Cloud Machine learning Certification.
About Google Cloud (ML) Certifications
I have good news for you. First, I think I have gone through a number of resources over the last many months so you don’t have to, and I have collected some easy-to-digest VISUAL Guides, Videos, Demos as well as Courses and Notes to help you get started.
If you’re still early on your own journey into ML and AI, consider doing the Google Cloud Data Engineering Certification (It’s not too late!)
Stage 0 — Data (Engineering) -> (Data Engineer Certification)
Stage 1 — Model Training (ML)->(ML Engineer Certification — You’re here!)
Stage 2 — Model Use (AI) -> (TBC/WIP Certification)
Here is a Roadmap Reference for your Development & Certification Journey
Ok. More Good News. If you’ve got experience working with Google Cloud, or other Google Cloud Certifications or particularly those related to Kubernetes — you’re starting way ahead.
You may have saved yourself a couple of weeks off-the-bat. Learn all about the actual Google Cloud ML Certification Requirements here — Do Attempt the sample questions. Good or bad, This will be a good baseline for you to know what to expect and appreciate your own knowledge levels.
Lets’s Talk Motivation. Like, Why Bother?
I firmly believe, in line of Maslow Hierarchy of Needs (with Simon Sinek’s perspective) and wants, you need that Purpose to keep going. As I mentioned above, its circa 2 months of reading and covering this material! Sure, you can memorise some content, but to make it real, you need that Big Hairy Audacious Goal — even to manage yourself.
“What Business Problem are you trying to solve/Aspiration Goal to achieve”
#GenerativeAI disruption, and thus opportunities help — it’s a good way to start here.
Inspiration Option One: Know Things & Think Things & Have Coffee
#GenAI (And Machine Learning relevant) is eating everything. Learn how it works. Time to Get Excited! — It’s more like a starting Stage 0.5 — but works a treat.
Here are some fantastic resources — learning paths— https://www.cloudskillsboost.google/paths — More below:
Inspiration Option TWO — Grow Your Career!
Machine Learning (and AI, by Extension) Jobs are in the Top 10 Technology jobs — the trend is here to stay. Note that the Learning and Certification in Machine Learning is more foundational and fundamental — but Generative AI applications is an extension of ML, albeit not the same thing.
e.g. While I may be running LLama 3 on my mach and construct further use cases, but I don’t necessarily need to deep-dive into further ML training and optimisation — I can simply stick to off-the-shelf and local or remote models for inference and be done with it. Think AutoML capabilities with GoogleCloud, Search and and Conversation, CCAI, and spend my professional life integrating those components alone with a world of customisations.
Inspiration Option THREE — SWAG!
It may not be obvious, and event as a small gesture, and for the Google Cloud Fans, — it’s always well appreciated. The Swag is real, and a great conversation starter, and something to show off for all your hard work. IMHO I do it for the swag 🤘
Join the VIP Certified & Swag Community
Right, The Actual Learning Effort
This is the section with my notes, and great sources. Remember, it’s about #learningEveryDay — Don’t worry about the “2 months” recommended training timeline — It’s not all about Boiling the Ocean on day one. 😣
Now, The Google Cloud Machine Learning Exam is a cornerstone for those looking to validate their expertise in machine learning models on Google Cloud. As AI reshapes industries, this certification ensures you’re equipped to handle and harness the power of machine learning, driving innovation and efficiency in your projects.
Here I broken down the learning resources, notes by Knowledge Transfer Effort.
Now, Imagine you have half hour or so…. there is very little chance you’s be able to absorb net-new deep technical content and make sense of it.
I’ve attempted to break it down into Easy — Big Picture, Medium — Some Details and Hard — for Deep-Dive (it’s not Hard per se, as its relative to your starting point, but you get the point) Yet, Each Section covers the SAME Content Areas on ML topic, with adjusted Zoom Levels.
My ML Certification Prep, notes & resources;
Easy Start. Low Knowledge Transfer Effort
- Perhaps a little late, but I have come across fantastic Session by Cassie Kozyrkov at Google New York. Its from 2022, but concepts, Ideas and explanations I’d think to be rather timeless. You want to start here once you have Google Cloud basics in place
Medium Effort Knowledge Transfer — Some Details — to Deep Dive
The main course, almost like a backdrop to the entire Machine Learning Training and Certification for me was split across 3 resources predominately;
- #1 — I started Here — Google Cloud Skillsboost, as shared earlier. It’s also managed by Google. I always find really these easy start with, and a great options to dive in on topics with hands-on labs. You Earn badges and points for completing courses/Labs (Not just for ML but for Entire Google Cloud Ecosystem) There are labs from Beginner, to Advanced, to get you prepped for the exam.
- #2 — I Continued, Supplemented \w— Coursera — Machine Learning on Google Cloud Specialization. Managed by Google. Many Labs/Content is indeed the same as on Skillsboost. Handy feature is that you may earn a Coursera Certificate on your Learning journey as well. Thoughtful and helpful to keep you focussed and on track, while you update your Linkedin network :D on your steady progress. (This Full 8-module course will get Progressively harder as well, to get you prepped for the exam as well)
- #3 — After a while — Read The ML Book (See below). Just get it. Worth every Buck, Pound or Shekel. I Started Reading This book A month after I kicked of the hands-on Labs with Skillsboost. Now it may even make sense.
Model and Algos: Reference Material
DataCamp folks really did us a solid with a fantastic resource on the Machine Learning Cheat Sheet. It’s a nice summary what you need to know holistically across ML algorithms as you wade through Supervised vs Unsupervised ML and Training Options, as well as Loss Reduction for Model optimisation. Fascinating and super helpful to review before bedtime.
Other Videos for Visual Learning:
- Welcome To Machine Learning \w Cassie — Start Here (again)
- Learning ML in 2024 \w Boris Meinardus.
- AI Adventures \w Yufeng Guo @ Google Cloud Tech. AI Adventures explores the art, science, and tools of machine learning. Get started on your data science journey today! Subscribe to get all episodes as they come out → https://goo.gle/GCP
In-depth Articles and Guides that I found particularly helpful:
- Recent ML Exam experience and sample question walk-through to unpack the reasonsing behind those by Mikael Ahonen— Read More
- Best Practice Guide for Deep Learning — Dive Deep — Good to walk-through, you dont need to spend your weekend reading this one if it’s not your Jam. (For the Nerdy Few)
Deep-Dive — Knowledge Transfer Level High. The Book
This is the more hand-on reading, to be undertaken once you’re in The Learning Zone. Attempt it only once you’ve covered the Foundations, have hands-on experience or incredibly inspired to deep dive.
Professional Machine Learning Engineer Study Guide
It’s an amazing investment, and probably the first book I have purchased in a long long while. No, really.
This book is well worth it and covers not only the typical ML Content but also features the ⚠️ Exam prep Questions and Answers ⚠️
I found the exam hard, intense — and I would attribute a significant portion of my passing success to this book for sure.
Learning Strategy
This section is personal and hopefully, given all the context above and my learning journey you can see how it is similar your journey — or not.
My approach to this whole new topic was in following stages;
- Get Inspired. That North star, a vision will keep you working through the challenging labs — because you want to, not have to.
- Start easy. Play with GenAI, watch youtube playlist with the art of the possible.
- Get introduced to ML Concepts — watch Cassie’s Video sessions and Yufend in particular. Some of it may land and make sense, some of it may not. Thats ok.
- Get Started with the Course and Skillsboost — This is the practice Time. You should have reasonably background knowledge and reference to get going.
- Read The Book. This is a nice wrap around you can start after month one of your Hand-on courses. It will make more sense.
- Practice Exams. Book has it. Coursera has it. Google Cloud Website has them. Go through them all. Mind that some third party websites may have dated questions as well.
Conclusion
The path to certification might be challenging, but it’s also incredibly rewarding. As you prepare for your exam, remember that each study session is a step towards mastering the disruptive capabilities of ML which builds AI. I look forward to hearing about your journey and successes!
Hope you find the guide insightful, helpful and don’t forget to update your Certified Profile to get listed in the Google Cloud Certified Directory, Share your success on LinkedIn (I want to hear all about it) and Order That Swag
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