This article is also published at https://jiayu.substack.com/p/how-i-passed-googles-tensorflow-certificate
I passed the test for Google’s TensorFlow developer certificate back in April and got the certificate in a few weeks. This is a relatively new addition to Google’s developer certificates program, and I would love to share a bit more context on it without too much spoiler, hoping it’ll be useful to anyone who’s interested in taking it.
As an engineering manager, my own or my team’s daily job does not involve machine learning per se, so learning TensorFlow and deep learning in general is purely motivated by my personal interest. Throughout my career so far, I have not yet been in a position where I have to personally train a model to tackle a problem, but I’ve done some work with Kubernetes that empowers TensorFlow, as well as Coursera courses that involves using deep learning to empower self-driving cars, so that can explain where my interest comes from.
Also I’ve been reading textbooks, taking courses, running open sourced Colab notebooks, etc. for the last few years, so I feel like I can challenge myself and get my skills tested. Reading the test FAQ I can totally see that this is by no means a test for SOTA knowledge and skills, but fundamentals are important, right?
Since this is a test, like all other types of test in life, getting prepared is important. The most relevant information is on the official website, specifically the public FAQ document, and you should read it at least twice, because it basically covers what’s to be expected in the exam, and also a major hint on the shape and form (e.g. number of questions).
If you are like me who have taken the TensorFlow in Practice specialization by deeplearning.ai before, the test coverage should strike a familiarity here. If it’s not the case, it is strongly suggested that you take that specialization as a preparation, or just as an introduction on how to get onboard with the new TensorFlow 2.0 API. I spent about 8 hours in total going through the Colab notebooks in the course materials and it turned out to be most helpful.
Taking the test
The test is taken on your own computer (i.e. remote) with PyCharm and a plugin. I cannot go too much into the details, but an important tip is: basically all the rules on taking tests and interviews apply here: find a quite and comfortable place with AC power and easy access to water and toilet, etc.
My initial estimate was that I don’t need to use up the full 5 hour time window, because I was pretty confident that training a few toy neural nets won’t take that long. I mentioned toy neural nets because if I wore the test designer’s hat, it would be unnecessarily difficult to evaluate people’s work based on very complex models and small improvements made to basic models, i.e. getting everything working is more importantly than fine adjusting one model. (This actually applies to real production work, since establishing a baseline, even if you use just SVM or logistic regression, is also the first thing to do).
But then I was wrong. It turns out that I underestimated all the possible places where my code can go wrong. I believe I wasted more than 3 hours in e.g.:
- Fixing a cryptic exception message thrown from TensorFlow internals because I didn’t correctly specify input dtype (or do proper image reshaping), and that error message wasn’t helpful at all
- Failing to supply the correct combination of params to model.fit with image data generator so each epoch takes too much time (I am using my MacBook Pro so there’s no GPU)
- Trying to upgrade my model from a single layer of LSTM to two, but then my score went down from 3/5 to 2/5 (there’s no other feedback during tests so you can’t do error analysis), after which I can never get back to the original score for some reason
Luckily, after using up all the time I was table to pass the test! I really enjoyed the process since all the test contents were within my expectation and there was no surprise, meaning all my past learning and preparation really meant something. I did run into several problems but it did teach me a good lesson on how things can actually go wrong in real ML work.
After finishing the test, the result came in one or two hours, and you are then prompted to list your information on the developer directory. I imagine this would be useful for people doing ML job hunting but for me it is more of a social purpose. The certificate came in after more than two weeks later, but then there’s no other meaning than just a reminder to yourself.
This is by no means an end to the journey, as the criteria and content covered in this test is really just fundamentals. It helps to serve as a checkpoint, and a starting point to learn something more SOTA. For me the single biggest takeaway is (during my preparation) that I found out how useful Colab is and got used to working on it by default! In fact, I believe most people should be sufficed to work on Google Colab without buying any physical GPUs: you get data center level network speed with zero setup GPU/TPU support basically for free. Unless you want to train your models for days, e.g. retrain BERT from scratch, which you most definitely shouldn’t, it is definitely a more efficient choice.
So, is it really worth it?
To wrap up, I want to explore the question of whether it is really worth it, after all there’s a 100 US$ fee.
For people looking for ML jobs: basically you can think of it similar to buying a LinkedIn premium account: you’ll get more exposure, save some market discovery time and cost, but then again it’s you that they are looking for, the whole package. So getting that won’t buy you a job, but merely an entrance maybe.
Other than that, I find it a good excuse to push you to study for the materials, which is the truly meaningful part. If you have done that, then I wouldn’t worry if I didn’t take this.