Is the New Google Professional Machine Learning Engineer Worth It?

Quick Answer: The Google Professional Machine Learning Engineer certification is worth it for those invested in machine learning and AI, including many data scientists, machine learning engineers, and some software engineers. It validates advanced skills and expertise in Google Cloud's ML solutions, making it a valuable asset for career growth.

In just a few short years, artificial intelligence (AI) and machine learning (ML) went from the fantastical to the everyday. These days, it's not unusual to have ML algorithms running and solving everything from the most exciting and thought-provoking challenges to incredibly boring business needs. In fact, machine learning and artificial intelligence solutions are now so widely available to companies that it's rarely the question of whether or not it's possible to implement ML into business practices, but whether it's worth doing.

A Google Professional Machine Learning Engineer is someone trained to always ask that question first. If the answer is yes, they know all the best solutions for getting it done. Machine Learning isn't a one-size-fits-all solution, and it's certainly not a fire-and-forget solution either.

The Google Professional Machine Learning Engineer is earned by those who know how to architect ML solutions using the Google Cloud framework, which revolutionizes companies and solves some of their hairiest problems.

What is the Google Professional Machine Learning Engineer?

The Google Professional Machine Learning Engineer is an advanced IT certification maintained by Google. The certification proves that the person who earns it can train, deploy, monitor, and improve machine learning and artificial intelligence models. 

The Google Professional Machine Learning Engineer certification has two key emphases. First, the person who earns it knows how to design, build, and size ML models for solving unique business challenges. 

Second, the person who earns it is deeply familiar with all the Google Cloud technologies that make ML and AI possible, as well as the ML models and techniques considered industry best practices.

The Google Professional Machine Learning Engineer certification can be earned by someone working with machine learning but is also useful for people working in different career fields than applied machine learning and artificial intelligence. Knowing how to frame Machine Learning problems and architect their solutions is a skill that's useful for IT professionals in many different fields.

To earn the Google Professional Machine Learning Engineer certification, you'll have to pass one exam, which Google just calls the Professional Machine Learning Engineer exam. The exam was updated in October 2024, so make sure you're using updated training for training. 

What Does the Updated Google Professional Machine Learning Engineer Test?

The Professional Machine Learning Engineer exam has six sections designed to test your ability to train, retrain, deploy, schedule, monitor, and improve ML models. The six sections of the exam are:

• Section 1: Architecting low-code AI solutions (13%)

• Section 2: Collaborating with and across teams to manage data/models (14%)

• Section 3: Scaling prototypes into ML models (18%)

• Section 4: Serving and scaling models (20%)

• Section 5: Automating and orchestrating ML pipelines (22%)

• Section 6: Monitoring AI solutions ( 13% )

It's important to remember that the Professional Machine Learning Engineer certification isn't just from Google; it's also about Google. Not only does it test you on the abstract ideas and concepts of framing, architecting, and improving ML models, but it also tests you on doing all of it with Google tools. 

That means it's not enough that you understand Google Cloud ML tools really well. You also have to understand the concepts behind ML and AI solutions and what Google does uniquely in that space.

How Much Does the Google Professional Machine Learning Engineer Exam Cost?

It costs $200 to take the Google Professional Machine Learning Engineer exam. Once you've paid for and passed the exam, there's no further cost. You don't have to pay to apply for the certification; it's granted to you upon completion of the exam. 

The Google Professional Machine Learning Engineer certification remains valid for two years. After that, you'll have to retest the current exam. Google offers a 50% discount for your recertification exams, so although it costs $200 to earn the cert, it only costs $100 every two years to maintain it.

What Experience Do You Need for the Google Professional Machine Learning Engineer?

Passing the Google Professional Machine Learning Engineer test requires extensive experience with designing, building, and productionizing ML models. Additionally, you'll want to have a lot of experience using Google Cloud technologies doing those tasks. Google suggests 3+ years of industry experience, including 1+ years designing and managing solutions in the Google Cloud. 

The Google Professional Machine Learning Engineer exam now emphasizes your ability to build, evaluate, and optimize machine learning models using Google Cloud technologies. The focus is on managing large, complex datasets, creating reusable and repeatable code, and ensuring that models align with responsible AI principles and fairness throughout the development process. Collaboration with other teams to guarantee the long-term success of ML-based applications is also a key component.

Beyond designing reliable, scalable, and highly available ML solutions, you are expected to handle data pipeline creation and metrics interpretation and be familiar with foundational MLOps concepts like model deployment, orchestration, and monitoring. 

A significant part of the exam involves understanding model architecture and distributed data processing tools, which are essential for developing scalable, performant solutions. You'll also need a solid understanding of application development, infrastructure management, data governance, and how to automate and orchestrate ML pipelines.

While programming is a core skill, the exam does not directly assess coding ability. A basic proficiency in Python and Cloud SQL is sufficient to interpret the code snippets you might encounter. Instead, the focus is on higher-level competencies, including:

• Architecting low-code ML solutions

• Collaborating across teams to manage data and models

• Scaling prototypes into fully productized ML models

• Serving and scaling models for real-world use

• Automating ML pipelines and monitoring the entire lifecycle

• The ability to train, retrain, and improve models while ensuring they remain scalable and efficient is critical to passing this exam.

Who Should Take the Google Professional Machine Learning Engineer?

The Google Professional Machine Learning Engineer is an advanced certification that is a capstone for many machine learning engineers. But it's also a good certification for people who aren't solely in the machine learning career—data scientists and software engineers should consider taking it, too.

Is Google Professional Machine Learning Engineer Worth it for Data Scientists?

For data scientists and data analysts, the Google Professional Machine Learning Engineer is often worth it. Obviously, there are many different "flavors" of data scientists, but generally speaking, your career as an analyst will be improved with a certification that says you understand how to develop machine learning models and how to train them to do the work of dozens, hundreds, and even thousands of other analysts.

Data analysts who earn the Google Professional Machine Learning Engineer find it worth it because when you know how to visualize huge data troves and build your own data pipelines, you can use machine learning and artificial intelligence to speed up your analysis and uncover opportunities you'd never find on your own.

Is Google Professional Machine Learning Engineer Worth it for Software Engineers?

The Google Professional Machine Learning Engineer requires a solid foundation in math and computer science, which is why many software engineers are finding the Google Professional Machine Learning Engineer to be worth it. Earning the cert depends in large part on the skills and knowledge they already have to make themselves successful in software engineering, but it also lets them expand their foundation to include machine learning and artificial intelligence models.

ML and AI depend on many different languages, such as C++, Java, Python, R, Lisp, and Prolog. Since so many software engineers are already developing in those languages, machine learning engineering is often a short step away.

Is Google Professional Machine Learning Engineer Worth it for a Machine Learning Engineer?

Yes, the Google Professional Machine Learning Engineer is definitely worth it for someone already working as a machine learning engineer – or someone who wants to become one eventually. Since the Professional Machine Learning Engineer exam tests you in equal parts on developing, training, and improving ML models as well as the Google Cloud products and services that enhance and enable those models, it's a great certification if your network depends on Google products.

Google certainly isn't the only name in the ML/AI world, but it's one of the leaders. With this certification, you prove that you have mastered their tools and services, which can unlock unprecedented big data solutions for unique business concerns.

Bottom Line: Is the Google Professional Machine Learning Engineer Worth It?

Yes, for people who have already invested heavily in the machine learning world, the Google Professional Machine Learning Engineer is definitely worth it. It's too advanced of a certification for newcomers to the complex math, programming, and problem-solving that goes into ML/AI. Still, it's a great certification for people looking to solidify their resume with a Google-sponsored certification or round out their skills and experience in machine learning.

Using Google Professional Machine Learning Engineer Certification to Learn Skills

The Google Professional Machine Learning Engineer tests six distinct skills that a machine learning engineer must have to be successful. In each case, it also tests their familiarity with Google Cloud services that enhance them.

Do you know how to translate a challenge your business faces into a formal ML problem, then define its success criteria and design a reliable, scalable, and highly available ML solution that will make the best use of its hardware and architecture? Can you build ML models, train them with a wide array of file types, test them, and scale them up and down? Are you sure your training pipelines will be implemented without any problems and that you can track and audit the metadata that comes off your pipeline runs?

If you feel even a little shaky on any of those skills, then preparing for the Google Professional Machine Learning Engineer exam is a perfect way to master them.

Using Google Professional Machine Learning Engineer Certification to Validate Skills

The right company is willing to pay an arm and a leg to the IT engineer who can develop an AI that solves their problems and anticipates their needs. The Google Professional Machine Learning Engineer certification says to an employer that as far as Google is concerned, there's nothing ML or AI that you don't know how to manage. That's a strong endorsement and can lead to jobs, promotions, and exciting jobs.

Get Ready for the New Exam 

The updated Google Professional Machine Learning Engineer exam shifts its focus from traditional ML tasks to a more integrated approach emphasizing MLOps, automation, and production readiness. 

The new version highlights team collaboration, responsible AI, and the ability to scale and manage models over time. Coding proficiency plays a lesser role, with a greater emphasis on automating workflows, ensuring long-term success, and building scalable, repeatable solutions. 

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