Carving a Machine Learning Engineer Certification Path

A Professional Machine Learning Engineer designs, develops, and produces ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The Machine Learning Engineer is proficient in all phases of model architecture, data pipeline interaction, and metrics interpretation and should be familiar with application development, infrastructure management, data engineering, and security.

Google Professional Machine Learning Engineer Certification

Professional Machine Learning Engineer is this certification provided by Google cloud in the area of machine learning. According to the Google cloud, an experienced Machine Learning Engineer always helps design, build, and productions machine learning models to solve complex business challenges using Google cloud air technologies and all the knowledge of the machine learning models.

Professional Machine Learning Engineer exam assist you in assessing the following skills:

  • Frame machine learning problems.
  • Architecture machine learning solutions.
  • Prepare and process data.
  • Developing machine learning models.
  • Automate and orchestrate machine learning pipelines.
  • Monitoring, optimizing and maintaining machine learning solutions.

Learning Path to Get Google Professional Machine Learning Engineer Certification

Google itself has a learning path defined for machine learning programs to understand machine learning easily and simultaneously prepare for the Google Professional Machine Learning Engineer certification. Based on the portions of the syllabus present for the certificate, you can opt for all training available in the machine learning path by Google to prepare for the certification exam.

To give this exam, Google recommends you have at least three years of experience in machine learning to have a proper understanding while preparing for the exam.

This learning path will offer you neural networks, TensorFlow, and Google Cloud Machine Learning Engine. Even if you do not have any previous experience with machine learning, that is okay because these courses cover the basic concepts.

The first course describes the fundamentals of neural networks and how to achieve them using TensorFlow. Then it shows you how to train and deploy a model using Cloud ML Engine.

The second course teaches how to build convolutional neural networks, which effectively perform object detection in images, among other tasks. It also explains how to visualize a model’s show using TensorBoard, reduce overfitting, and train a model on a custom cluster using Cloud ML Engine.

Both of these courses involve hands-on demos you can do yourself. Then you can test what you have learned by taking the exam.

Also Read: Things to Remember for Your Google Professional Data Engineer (GCP-PDE) Certification

Preparing for Google Professional Machine Learning Engineer Certification

There are different methods to prepare for this exam. Google advised some of the steps to be performed before the exam to prepare for the certification.

The levels suggested by Google are as follows:

  • Get the Real-world Experience: To have real-world experience on machine learning projects so that you can have a better knowledge of machine learning technology and terminologies.
  • Understand What Is On the Exam: what topics will be on the exam so that you can study efficiently we have already covered this part in the previous section.
  • Review the Sample Questions: Google already has a place where they have posted a sample question according to an exam; you can look at that sample questions to prepare yourself and solve some model questions or mock exams.
  • Round Out Your Skills with Training: It is better to practice all the services provided by Google Cloud for machine learning to have a better hands-on experience and understanding.
  • Schedule a Machine Learning Engineer Exam: After you have made all the above steps, you can now schedule an exam according to your availability and readiness.

Career for AI and ML in Google

Most of the people working in Google in Machine Learning and AI aren’t indeed developers, but most are Research Scientists. This means they hold a Ph.D. degree and have a considerable amount of research experience. Many branches of Google, such as Google DeepMind, have a minimum education qualification element of a Ph.D.

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Talking about developers, Google’s software engineers/developers/programmers work in different departments and fields. So, if you need to join just as a developer, its way is the same as any other programmer position.

It is no surprise that the Artificial intelligence talent market is white-hot at present. Gartner maintains that the business value of AI will stand at $3.9 trillion in 2022, while IDC predicts that the worldwide spending on cognitive and artificial intelligence systems will reach $77.6 billion by 2022.

Career Paths for Machine Learning Engineer are:

  • Machine learning engineer
  • Data scientist
  • NLP scientist
  • AI/ML developer
  • And many more

Organizations working with google AI and ML technology are as follows:

  • Bright star
  • Geotab
  • Blazeclan
  • Therapy and more


Certificates are not the end-all-be-all, but the new Google Professional Machine Learning Engineer certificate is an excellent option for professionals seeking to advance their careers.



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Amaaira Johns

Amaaira Johns

Highly experienced in Cloud and Computing with a strong marketing and sales background. Certified VMware professional with 10 years of business experience.