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Data Science Goals: Take Your TensorFlow Developer Certificate Exam Now!

With COVID-19 forcing staff to work from home, it’s time to recalibrate Data Science objectives by taking the TensorFlow Developer Exam.

In the last few years, Artificial Intelligence, Machine Learning, and DevOps projects have grown to become the most promising career avenues. It not only pays well but also provides ample opportunities to make a significant impact on Digital Transformation journeys for enterprises and SMBs. In the last 18 months, we have learned the importance of AI and Machine Learning and its role in transforming business infrastructure. Today, we see thousands of IT professionals signing up for Data Science courses to join the Big Data Economy. And, with COVID-19 forcing staff to work from home, we decided to recalibrate Data Science objectives for 2020. And guess what, TensorFlow Developer Exam features right at top of our list.

TensorFlow Developer Program is one of the most popular certification courses for DevOps and AI engineers. The certificate program allows Programmers and Coders to demonstrate their proficiency in building sophisticated Deep Learning and ML models to solve complex problems.

Today, TensorFlow DevOps Community contributes immensely to the AI ML ecosystem. We are creating impactful avenues and opportunities for all stakeholders with a common goal of building AI responsibly and ethically.

What is TensorFlow Developer Certificate Exam?

TensorFlow Developer Certificate Exam is a Level-1 certification exam for DevOps and Deep Learning modelers. The certificate grants recognition to candidates after thoroughly testing the foundational knowledge and skills of integration AI ML models into tools and applications.

TensorFlow, developed by Google Brain, Google’s AI lab, offers the TensorFlow Developer Certificate program. The exam is conducted to test DevOps skills of registered TF developers around the world. Passing this exam would mean earning a formal recognition of AI ML skills.

In an official blog, Alina Shinkarsky, on behalf of the TensorFlow Team, wrote –

“In the AI world today, more and more companies are looking to hire Machine Learning talent, and simultaneously, an increasing number of students and developers are looking for ways to gain and showcase their ML knowledge with formal recognition. In addition to the courses and learning resources available online, we want to help developers showcase their ML proficiency and help companies hire ML developers to solve challenging problems.”

Technologies Included in Certificate Exam

To be recognized as a TensorFlow Developer, a candidate has to prove skills and understanding of various TensorFlow models. These models are applied to technologies used in–

  • Computer Vision
  • Convolutional Neural Networks
  • Natural Language Processing, and
  • Image Recognition and Processing Data Analytics and strategies.
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Preparing for the TF certificate exam would mean working extensively with the foundational principles of AI ML, Big Data Analytics, and Deep Learning. A skilled TF DevOps engineer would be certified in building ML models in TensorFlow 2.x that could be used in numerous emerging new-tech applications such as object detection, text recognition algorithms, and convolutional neural networks.

Recommended: AiThority Interview Seth Siegel, North American Leader Artificial Intelligence Consulting At Infosys

In short, DevOps professionals can become a part of the widely-acclaimed and fast-growing TensorFlow Ecosystem and Community that are pushing the bar higher in AI ML engineering.

TensorFlow Ecosystem
TensorFlow Source: Ecosystem

How to Prepare for the Exam?

TF Developers can prepare for the exam by learning popular Programming languages used in AI ML modeling. Introductory and advanced skills in Python programming is the most basic step for TF Developers preparing for the exam. Candidates with strong analytical and mathematical skills are likely to pass the exam with respectable grades. A background in working with complex mathematical and statistical specializations is a perfect way to master the TensorFlow Developer Certification.

TensorFlow certification automatically qualifies you to get absorbed into the ecosystem of powerful add-on libraries and ML modeling. A career with data teams working extensively on features like Keras Functional API, Model Sub-classing API, Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT seem too good to be lost.

TensorFlow Trusted Partner Pilot Program is also a breeding ground for an active group of developers, researchers, visionaries, tinkerers and problem solvers. TF certification would help you to gain further traction in the AI-driven economy, which I think would be further accelerated once the ‘Lockdown Effect’ subsides.

Requisites to Take the Exam

TF Developer Certificate Exam is an online, performance-based test that can be taken on a PC or Smart Device that supports the PyCharm IDE requirements. The exam takes anywhere between 4-5 hours to complete, requiring the candidate to implement TensorFlow models using TensorFlow within a real-time PyCharm environment.

This Handbook will be useful to learn more about exam preparation.

With a month’s practice and dedicated programming practice, a DevOps engineer can crack the exam. Once you pass the TF Developer Certificate Exam, TensorFlow will provide an official TensorFlow Developer Certificate and badge. You can showcase your TF skill set and share on your CV and LinkedIn, GitHub and the new TensorFlow Certificate Network!

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