Sorry, the TensorFlow Developer Certificate is Pointless

Plenty of Better Alternatives Exist to Prove your Skillset

Photo by Daniel Mingook Kim on Unsplash

Google’s overall openness and investment in the space of AI has been phenomenal. I really think that unequivocally, the whole world has a lot to thank them for. Academic breakthroughs are published and code is often made free on GitHub. What more could anyone ask for?

Google are impressive.

Given my respect for their work, I was taken by surprise when I heard about the new TensorFlow certificate. So were responders on Quora. So were a few people on Reddit.

Now two achievements that changed my life were (a) teaching myself to program and (b) teaching myself enough Machine Learning to get into my chosen masters program. My academic background at that point was Mathematical Economics for which ‘STATA’ was the closest I’d been to programming — so the learning curve was steep and fast.

Teaching myself AI was hard

It’s no surprise when I say that AI (whatever you want to call it) is a gruelling discipline. Few other subjects cross the divide between Mathematics, Statistics, Computer Science — not to mention domain specialities within Linguistics, Imagery and more.

Moreover, domain experts with experience can attest to every new AI problem being different to existing. So to realise great results, you have to be creative with the deepest parts of academic knowledge. ‘Curriculums’ rarely go that far in creativity.

In what follows, I give 4 issues that I have with this certification.


Issue 1 : The Certificate Involves No Mathematics

Feed a man a fish, he’ll eat for a day. Teach a man to fish, he’ll eat for life.

Studying the Mathematics behind Machine Learning and AI is really, really hard because the level of understanding required is very advanced. However without going to these depths, you’ll simply make mistakes.

For example, understanding the distributional assumptions behind a model is imperative to make good judgement on which model to use. Changing assumptions about the distributional properties of your noise, for example, can alter your entire optimisation procedure (from closed form solutions assuming normal distribution to Laplace approximations assuming Laplace distributions).

Moreover, certain models are not suited for certain problems and you can only understand this if you understand why. Take the example of vanishing gradients: this invalidates the use of Neural Networks in certain problems.

Issue 2: Citizens who live in Sanctioned Countries are Excluded

This is something a bit more political but the beauty of MOOC’s and general online learning is that it doesn’t discriminate on location.

However, residents in the following “sanctioned countries” are not eligible for to take this exam: ● Cuba ● Iran ● Syria ● Sudan ● North Korea ● Crimea Region of Ukraine.

Now, these individuals are free to take the course outside of their country, but I don’t think it’s as easy as ‘just leaving the country for an exam’, for many people in these countries would struggle to even do so.

I understand that these countries are on a sanctioned list but I don’t see why education is being restricted here. If the law is really inhibiting them being examined, an alternative should be put in place in the spirit of complete open accreditations.

This is from TensorFlow’s Handbook [Page 7]

I really don’t understand this position as you could argue that the Students in these ineligible countries deserve these exams more than in developed nations.

This sanctioned list defeats the purpose of open accreditations.

Knowledge doesn’t acknowledge borders and neither should we.

Photo by Timo Volz on Unsplash

Issue 3: The Certificate has Fee’s

Fee’s are a great divider in society and despite the stipend provided by Google to help Students from disadvantaged backgrounds, the author of this article still spends over $200 for the whole package.

It’s not that I have a problem with charging people for education: I think that Skillshare and Coursera are fantastic. However, the benefits to education have to be exceptionally clear whereas in this case, it’s not. There are a variety of Summer Schools and MOOC’s with much have better value-propositions, and these should definitely be explored.

Issue 4: There are better ways to prove your worth

Ultimately, the choice of spending over $100 on an exam and a lot of hours on trying to pass the exam that gives you an unproven edge is a decision that each person has to make independently.

However, having been through a lot of interviews and having interviewed a lot of candidates, I can firmly tell you that if you nail a few of the following, they’ll be much more useful:

Summer Schools or MOOC’s

Assuming you’ve taken away all the mathematics required to build ML models from scratch, built a network whilst studying and reached out to key people in these eco-systems. Even for MOOC’s, large online forums exist where you can network and make friends with people who can help.

Kaggle Competitions

There exists a lot of kudos in the industry for scoring highly here. Seriously, it goes along way to say you’re in the top 1% of Kaggle for a particular competition.

GitHub

From a companies perspective, there’s nothing better than showing off what you can do for a prospective employer. Make a Jupyter Notebook and show how you would solve a problem in their industry. If you can’t get real data? Generate it using Monte Carlo. Recreate results from Academic Papers and show how you’d improve on their results. This is what you would do on the job anyways.

Contributing to Libraries

People who contribute to core ML libraries are rated very, very highly in industry. Yes, you have to be a good coder but reaching out and offering your time/fixing things goes a long way here.


As with all decisions in life, if the situation you’re in forces you to take the exam then you should definitely take it. However, if your argument is ‘I think this will help me but I’m not sure exactly how’ then I think you should look at the issues raised and see which alternatives make more sense.

I think the rationale for the qualification may have some merit (to unify some accreditation in an open-source manner), but it’s obscure syllabus that lacks any form of mathematics is something I really can’t get along with.

The high bar of requirement in Artificial Intelligence is there because the work is hard. It takes a long time, grit and determination to make it through the process.

Trust me: it’s worth taking the long-way round and doing it properly. Your life will change for the better.


Thanks for reading again!! Let me know if you have any questions and I’ll be happy to help.

Keep up to date with my latest work here!


Links to TensorFlow’s Developer Certificate

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