Who Should Take TensorFlow Certificate?

In early 2020, TensorFlow, an open-source and end-to-end platform used to build and deploy machine learning models released a professional certificate program with the goal of helping developers to be recognized for their skills.

Since then, the certification exam has gained a lot of attraction. In this article, you will learn about the certificate exam, who should take it, what that means to you and your career, and the next step if you are interested in the exam.

Overview:

• What is TensorFlow certificate?

• Who should take the certificate exam?

• What are the benefits of taking the exam?

• Interested, what is the next step?

• Conclusion

What is the Certificate Exam?

To help TensorFlow developers show their expertise in building highly effective deep learning and machine learning models, while also setting themselves apart, Google launched TensorFlow Certificate Program.

To pass the certificate exam, the candidate are required to have a prior experience in building Computer Vision, Natural Language Processing, and time series models, all working with real world datasets.

The certificate exam costs 100$ and takes 5 hours to complete. To learn more about the exam, we recommend you to check out the Candidate Handbook.

Who Should Take the Certificate Exam?

If you're reading this, you are probably interested in the exam and may be wondering if this is something for you. Whether you want to learn a new skill or verify your expertise, adding this recognition to your profile won't hurt.

The certificate exam is not limited to specific kinds of people. If you can check yourself in the following categories, you can take the exam:

• Aspiring Machine Learning Engineers who want to show the proof of their TensorFlow skills

• Aspiring Data Scientists who want to add TensorFlow recognition to their career profile

• Students, Developers, and Python Programmers who want to learn and get recognized for their efforts to master TensorFlow

• Experienced Machine Learning and Data Scientists who want to a proof of their TensorFlow experiences or who wants to add TensorFlow to their toolbox.

Whatever your category, taking the exam won't hurt. The following are the reasons.

What are the Benefits of Taking the Exam?

Taking and passing the certificate exam comes with different benefits, all which can take your career to the next level.

Here are those benefits:

• To be on the worldwide networks of TensorFlow Developers: Joining the network of thousands of certified developers can be a boost to your career and visibility as well. Take a look at the certificate network.

• To make a career transition or improve your existing career: TensorFlow certificate is a well-respected exam and it's new. There are a lot of career advantages ranging from getting a new AI job or a raise to your existing career.

• Boost your resume: Not only you will get attraction by being on certified network and professional social media like LinkedIn, this is a sure thing to be on your resume.

• Learn something new: This is all time benefit, it's always good to invest in yourself through continuous learning and assessment. You will not regret it paying hundred dollars to get useful skills.

Now you maybe wondering the next step after you have learned about the certificate exam and found potential in it.

Interested, What is the Next Step?

If you are interested in the exam, the number one resource is the official Candidate Handbook. If you have been using TensorFlow, you will see what you lack after going through the handbook. If you are also looking to refresh your skills, you will find the relevant courses on popular learning platforms such as Coursera and Udemy.

The Python Academy provides an in-depth TensorFlow Training Bootcamp whose goal is to help you learn practical skills and pass the certification exam. The training is conducted live by Experienced TensorFlow Instructors. If you find it hard to manage your learning, you may want to look into the training.

Conclusion

Certificates are often undervalued in technical careers, and this is true for many junk certificates but surely, that is not true for Google TensorFlow Certificate. To motivate the potential of the certificate program, the below photo is the end of this article. It was taken from the Google I/O Event.

source: Google I/O ML Keynote