6 things to do after passing the Google Machine Learning Certification Exam

How to make the most of your ML certification

Paul Kamau
7 min readOct 10, 2022

For many people, hard math followed by equations that make little to no sense are the first things that come to mind when they think of Machine Learning. That, and maybe killer robots too.

So if you’ve gone through this journey and achieved your certification, CONGRATULATIONS, you deserve it. Let me repeat that, you deserve that feeling. The ML certification test is the one of the HARDEST exams with the greatest opportunities in the market you can take. Don’t let the Network engineering folks take that from you.😁 So I know how you feel right now. Trust me. I still remember that feeling when I passed my ML certification back in May of 2022 and the confidence boost, that sense of validation that I was on the right path. This is especially true because this was something I had taken up on myself. My job didn’t require it, no one placed this requirement on me, but I knew I wanted to grow in this field.

Consider this fact:

Although private investment in 2022 is set to exceed $135 billion, overall investment in AI research and applications is set to reach $500 billion by 2024.

There are different motives for pursuing Machine Learning. I’m personally excited about the future of technology with AI at its center. And maybe that you as well. You want to grow, you want to show that you’re cut out for this, and are willing to go through the wringer to fight for it.

So maybe, the question on your mind is:

  1. What should I do next?
  2. Where do I go from here?
  3. How do I realize the full value of my new skills?

Here are 6 pieces of advice that have worked well for me. And these apply to any industry cert you’ve sat for, … even you Network engineering folks 😒. So keep on reading and let me walk you through that.

  1. Take that victory lap.
  2. Share your experiences and what you’ve learnt
  3. Stay immersed in the world of AI and ML. (Books, Podcasts, TV Shows)
  4. Go back and review the labs and materials you learnt while studying for your certification
  5. Grow your portfolio and learn new skills.
  6. Plan your career goal, trajectory and get to work

1. Take that victory lap.

Stop and smell the roses because there’s a ton of work ahead of us. Listen, don’t overlook your success or wait for others to celebrate you. You are your biggest fan and advocate.

You should savor and celebrate your accomplishments, right? Because you were studying for this test, you probably, just like me, skipped out on a lot of things like friends meetup, movie nights and didn’t go out much. Most of your time was spent prepping, doing practice tests over and over again, writing insanely long cheat sheets and watching lectures over and over to try to understand these concepts.

Therefore, now’s the time to put everything aside for a while and take that vacation you had pushed off. Make this a regular practice so that you don’t burn out.

So if you haven’t done this already. Stop reading this and go on your vacation / trip or do something you love! Or if you haven’t got enough sleep for a while, do that. Pick up old hobbies, recharge and center yourself again.

2. Share your experiences and what you’ve learnt

Having spent months studying for this certification, it’s pretty easy to forget how far you’ve come in terms of your knowledge and also how others would find value in what you now know.

There’s a famous quote by John C. Maxwell (The 360 Degree Leader: Developing Your Influence from Anywhere in the Organization) that says:

“You never really know something until you teach it to someone else.”

I’ve fallen into the trap of feeling like what I knew was too obvious and wouldn’t benefit anyone. But that’s not the case. There are people at different stages of their Machine learning journey and they can benefit from your knowledge. A lot of professionals and people who are not in this industry mix up AI and ML and use it interchangeably, others struggle to understand the difference between Machine Learning and Deep Learning. And so no matter where anyone falls on the spectrum of their ML understanding, you can benefit them.

This will be especially beneficial for you as you grow to have a deeper understanding of this field. I’ve benefited tremendously from sharing the snippets of knowledge gained from pursuing this. Blogging is one of them, Instagram another.

3. Stay immersed in the world of AI and ML. (Books, Podcasts, TV Shows)

SCI-FI shows based in AI are a core inspiration that picked my interest in the field of AI and Machine Learning. Podcasts, TV Shows, movies, blogs and books are great ways to stay immersed in this, in one form or another.

Here are a few books on my Bookshelf that I’ve enjoyed on the topic. The AI-First Company is a favorite by Ash Fontana followed by Eric Topol’s, Deep Medicine. I’ll share my favorite top 5 for Podcasts, Books, TV Shows and Blogs in a later blog.

Follow my Goodreads — technology list for my reads.

Follow for more book recommendations. (pauly.ai)

4. Go back and review the labs and materials you learnt while studying for your certification

Alright so you spent a few months studying and if we’re being honest, there’s probably a few labs and materials you skimmed over. That’s pretty normal and it happens. When I was prepping there were tons of labs I either rushed through or didn’t finish entirely and looking back, it had really good concepts to teach. Luckily, after passing my test, I’ve had a chance to go back and cover these and understand the core concepts including doing the whole things without referencing the lab notes. Additionally, I’ve used these labs as part of my ML portfolio, for which I’ll talk about in my next tip.

At this juncture, you have the benefit of hindsight and a new perspective on the knowledge area. Therefore, take the time to review your study notes and labs to really boost your confidence in solving these problems not just theoretically on the certification test, but with practical work to back it up.

5. Grow your portfolio and learn new skills.

There are many ways to grow your Machine Learning portfolio. An easy start is using the labs as the foundation of your portfolio. I’ve gravitated towards focusing on BigQueryML over Tensorflow at the moment and have built a couple of models that i learnt from my learning materials.

Applying BigQuery ML’s Classification, Regression, and Demand Forecasting for Retail Applications” is a great example of this. Multiple ML problems are solved in this single series and it does provide you a vocabulary to speak about your work, and the challenges you faced, and how you solved them.

Your portfolio must contain a VARIETY of problems and solutions, not just one domain or one technology. My ML portfolio contains a mix of technology-based approaches ranging from BigQuery ML solutions, Tensorflow, AI APIs followed by categorical algorithm solutions like image classification, GANs, Reinforcement Learning, Time Series, NLPs, etc.

Here’s my recommendation for BigQuery ML based tutorials and examples.

BQML Tutorials on Google Cloud

For TensorFlow based projects, I’d recommend these Keras based examples to build and incorporate into your ML portfolio.

TensorFlow based tutorials with Keras

6. Plan your career goal, trajectory and get to work

And last but not least, Plan your career goals, trajectory and execute on it. What do you want to do in 3 years? What roles do you want to move into?

In my career, I’ve always found myself at the intersection of business and technical teams. I love building apps and writing code, but I also enjoy being part of the business decisions that decide what we work on. I’m very fortunate to work at Google, which is the AI capital of the world. And I find a lot of inspiration in the AI work that Alphabet does especially with DeepMind.

You don’t have to work at Google to make your next move. There are roles from AI consultants to Engineers, to Ambassadors. If you’re a software developer, then you can do integrations and product enhancements with AI. Back in 2015, Google updated the search engine from the heuristics based model to a machine learning based system and it changed the quality of the searches dramatically. I’d recommend starting with companies that are working in AI, see the kind of roles they are engaged in, then find what you’re interested in.

Secondly, network with other people. This can be online or in person. Take advantage of the online community on Kaggle, or instagram, TikTok & YouTube that create content around AI&ML. It will be extremely valuable to connect with other people and showcase your work as you go along.


Let me know below and also if you have any questions, drop it in the comments below.

Here’s my next upcoming blog…How to build a great Machine Learning portfolio.

Follow on instagram for more snippets 👉 @pauly.ai

I share early career advice, mental health tips, tutorials on BQML, AutoML,TensorFlow, interview tips & regular Q&As 🚀



Paul Kamau

TAM @google | I write about tips, tutorials and impact AI & Machine Learning | https://bio.link/paulkamau