ML Engineering, and the Google Cloud Professional Machine Learning Engineer examination. This article will cover preparing for the exam, the costs associated with training and certification, and the pros and cons of getting certified.
Google Cloud Platform (GCP) is a suite of cloud services offered by Google that runs on the same underlying architecture that powers Google’s apps. These services provide clients (independent users and organizations) with access to a robust infrastructure, serverless tools, and enterprise-grade software, with minimal overhead time in installation and management. GCP provides competition to Microsoft’s Azure and Amazon’s AWS platforms.
While GCP has a ton of functionality that may power many different industrial applications, the use-case we are concerned with is Artificial Intelligence. As such, we will look at the AI-related tools offered by the platform in this article.
Furthermore, Google Cloud provides various certification paths for developers, engineers, and architects to showcase their prowess with building on the cloud. In August, Google released the Cloud Professional Machine Learning Engineer (PMLE) exam, in beta mode, that I took and successfully cleared. As of October 15th, 2020, the exam is no longer in beta; i.e., it is available to everyone. This article will cover preparing for the exam, the costs associated with training and certification, and the pros and cons of getting certified.
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While one may showcase their skills with TensorFlow in the form of projects, it is nearly impossible to demonstrate the same competence when working with the cloud; after all, how does one _demonstrate _their ability to use a tool? Therefore, I strongly urge you to consider taking this exam, even if you may not have wanted to take the TensorFlow Developer Certificate exam.
Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.
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