Announcing our new Professional Machine Learning Engineer certification
Certifications, Google Cloud
Machine learning (ML) is becoming an integral part of how organizations are run with more than two-thirds of businesses around the globe already using ML. However, finding employees with the right ML skills is one of the top concerns for IT leaders in 2020.
To help address this skills shortage, we’re offering the Google Cloud Professional Machine Learning Engineer certification. Now, cloud professionals can become industry recognized and demonstrate to employers their expertise in designing, building, and productionizing ML models to solve business challenges.
Feedback from the launch of the exam has been positive. Brian O’Connor, Director of Data Science at Pandera Systems, earned the Google Cloud Professional Machine Learning Engineer certification earlier this year as a part of the exam’s beta phase and specifically found the exam’s integration of machine learning operations (MLOps) as a marker that Google Cloud’s certification has its finger on the pulse of where the future of ML is headed.
“There’s a very high demand for the right tools and skills for MLOps, and Google Cloud is ahead of that curve by offering the necessary MLOps tools and training, “ said Brian. “Most corporations have data science teams who are successful at building ML algorithms to solve a problem. However, they’re currently having difficulties integrating those ML algorithms into their existing processes and infrastructure,” he said.
O’Connor expects MLOps to be a big trend in the future of data science just as DevOps became a massive trend for development teams years ago.
Why get the Google Cloud Professional Machine Learning Engineer certification?
Google Cloud certifications have measurable impact on careers and businesses. According to an independent third-party research organization, 87% of Google Cloud certified individuals are more confident in their cloud skills. Moreover, 71% of Google Cloud certified individuals report that becoming certified enabled or will enable their employer to get more business, increase work with existing customers, or help scale up their business.
The Machine Learning Engineer certification exam is a two-hour exam which assesses individuals’ ability to frame ML problems, develop ML models, and architect ML solutions. It also evaluates abilities to automate ML pipelines, orchestrate ML pipelines, prepare data, process data, as well as monitor, optimize, and maintain ML solutions. We recommend you have at least three years of industry experience, including one year of experience designing and managing solutions using Google Cloud before taking the exam.
How to prepare for the Google Cloud Professional Machine Learning Engineer certification exam
The certification exam is thorough and some of the material covered may be new to those interested in earning a Professional Machine Learning Engineer certification.
We recommend studying Google Cloud documentation, reviewing sample exam questions, and getting plenty of hands-on experience while preparing for the exam. You can explore labs on Qwiklabs and other course offerings in our ML learning path to gain more hands-on ML experience on Google Cloud.
Start getting more hands-on ML system development and operations experience by attending our November 20 webinar.
If you want to learn more about the Professional Machine Learning Engineer certification, sign up for our prep webinar on November 19 to hear from Lak Lakshmanan, Google Cloud’s Head of Data Analytics and AI Solutions, review sample exam questions, and more.