Become a Tensorflow Certified Developer — It’s easier than you think!

Starting out in Data Science and Machine Learning is still difficult because of the different approaches one can take and are suggested. Even more difficult is having to prove your proficiency to potential clients and recruiters.

While it may be easier to prove that you know what you’re doing when discussing with fellow developers, it is much harder to convince people of how good you are otherwise. Sure, participating in Kaggle/Zindi competitions and having projects in Github is a way to prove it, but someone who doesn’t know might fear that you copy-pasted existing code. After all, that’s what developers do, right? (inside joke, take it with a spoon of salt).

One objective way to showcase your skills is by becoming certified. However, most E-learning certificates like the ones on Udacity / Coursera, etc… aren’t trustworthy as one can easily repeat the exam till they get them. Those courses and certificated are meant for self-motivation and thrill of learning. They aren’t recognized per se. At the same time, certificates created by big names like Google, Amazon, IBM, and Microsoft hold much more value, at the cost of being a bit too advanced and geared towards that company’s technologies. Not really ideal for someone still fresh and starting out!

So What do I do? I’m glad you asked! Because now we have the Tensorflow Developer Certificate!


Yes, Tensorflow (TF). One of the most popular Deep Learning frameworks out there with PyTorch and Keras (although Keras & TF are now shipped together).

One main aspect of Tensorflow is that it’s geared towards deployment to production and thus focuses more on modularity, scalability, and pipelines. It’s the perfect tool to quickly train and deploy models at scale given the existing infrastructure. I’m not dissing the other frameworks, they’re better than TF in other aspects, but when it comes to quickly training and deploying models that can scale to millions of users, Tensorflow wins hands down. That’s why it’s so popular in industry and an excellent starting point for new Deep Learning enthusiasts.

The cool thing is that once you learn to code in Tensorflow, it’s easy for you to either switch to other frameworks specialize in an aspect of ML, both allowing you to then target more difficult certifications.

Seems cool! So how do I get certified?

Ooooh, I see you’re all fired up! Alright, let’s get right into it then. The steps to take the $100 certificate are:

  • Go on the official Certification Website and read through the website
  • Read very carefully the Candidate Handbook, it contains all you need to prepare for it.
  • (Optional) take the Tensorflow course on Coursera. It will greatly enhance your chance of passing the exam!
  • (Optional) You can apply for an education stipend which gives you free access to the above-mentioned course + brings the price down to $50
  • Register for the exam. Once you do, you have 6 months to take it. After that, you’ll need to register (and pay) again.
  • Once you pay, you’ll get a new handbook, this time more detailed. Read it even more carefully
  • Install PyCharm and in it, the Tensorflow exam extension. Both of these are required.
  • Once you’re ready, click on begin exam and you’ll have 5 hours to work on it and submit your exam.

What should I expect?

The exam is practical, you will work on regression, classification, Computer Vision, Natural Language Processing, and Time Series. Nothing too difficult, just enough to demonstrate proficiency in the framework and mindset. You will be graded on the quality of your models, that’s all I can tell you.

Any tips on how to get it?

All of us who took the exam signed an NDA so I can’t tell you what’s exactly in the exam, however, I can give you the most important pieces of information to remember that are already public:

  • You are free to use whatever resource you normally use while coding. This means you can use Google, StackOverflow, GitHub, etc. as well as Google Colab, GCP, AWS, etc. Everything goes as long as you can download the .h5 model into your local computer and put it in the right folder.
  • Even though you’re allowed 5 hours, you probably won’t need it. It is to take into account slow computers and bad internet connection. So if you use Colab you’ll at least win big on training time.
  • Get used to writing code, not copy-pasting. While it’s tempting, the benefit of writing your own code is that it builds muscle memory in your fingers and creates a mental roadmap that allows you to start new projects autonomously.
  • Finally, do what it takes to take the Tensorflow Developer Course. It’s very similar to the exam.

I took the exam. That wasn’t so bad. Now what?

you’ll receive an email in the coming days (sometimes less) with your results. If you did well, and I sure hope you did, you will get a certificate, a badge, and your beautiful face on the Certified Directory.

Examples of what you get after passing the Tensorflow Exam. It does feel cool!

Congrats! You’re now international :) Now go share your achievement with your community and continue leveling up your conditions. Don’t forget to share this article with the people that now want to become like YOU!




Experts on various Google products talking tech.

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Elyes Manai

Elyes Manai

Google Developer Expert in Machine Learning & Nvidia AI Instructor

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