Google Cloud Certifications: Cloud Digital Leader and Professional Machine Learning Engineer

Photo by Tima Miroshnichenko:

Cloud Digital Leader

Google’s decision to provide this entry-level certification may be the single most important thing the company has ever done to get users into the GCP ecosystem.

In Azure, we have the AZ-900. These certifications are presently available in the beta form: Microsoft Azure Fundamentals, Amazon Web Services (AWS) CCP, and Google Cloud Digital Leader (CDL).

Certifications like this one are quite uncommon to demonstrate one’s competence. Nearly everyone, instead, makes use of them as a learning tool. A non-technical entry-level certification like this is a great way to get started in the learning process, whether you need it for your work or are simply interested! We can do it because it’s doable, targeted, useful, and non-threatening. You can’t beat it!

If you’re not a techie, a certification like this may provide you with a solid foundation for learning about the public cloud in general and the Google Cloud in particular. Understanding how it may simultaneously lower overall expenses and greatly boost company agility will be necessary.

What is the role of a Cloud Digital Leader?

“A Cloud Digital Leader can describe the capabilities of Google Cloud key products and services and how they help enterprises,” as the company puts it on its website. There are several typical business use cases that the Cloud Digital Leader may explain how cloud technologies can benefit an organization.”

According to Google’s test guide for the certification, this individual should be aware of the tradeoffs between “costs vs responsibility” and “between the degree of administration versus freedom when evaluating cloud services,” according to Google’s test guide for the certification. It also touches on Google’s and our Joint Responsibility for System Security and Other Aspects.

If you’re technical, why bother with this certification?

Imagine if everyone in your office knew the fundamentals of cloud computing. You might save time and effort by not having to explain the same things repeatedly and by not having to deal with misconceptions caused by preconceived notions about how technology formerly worked. Understanding technology and communicating effectively about it are made possible by being able to speak “the same language.”

Professional Machine Learning Engineer (PMLE)

When it comes to the Professional Machine Learning Engineer, we believe it will become one of Google’s most sought-after credentials—even for those who have no intention of using the Google Cloud Platform. (At least not yet.)

The duties of the Professional Machine Learning Engineer go well beyond those of the Data Engineer in terms of responsibilities. So let’s take a deeper look at what else is out there.

What do machine learning engineers do?

According to Google’s write-up, this is how it works:

Proficient Machine Learning Engineers used Google Cloud technology and established ML models to tackle business difficulties. The ML Engineer needs to be knowledgeable in model construction, data pipeline interaction, metrics interpretation, and infrastructure management.”

The phrase “productionizes” is catchy, but the important part is that the ML models are designed and built and trained and used, just like the PDE.

As this Professional ML Engineer, a “proficient in all elements of” machine learning is described as being everything. And it isn’t limited to information about Google Cloud. That’s the most important thing you need to know about the certification. This certification is a Google Cloud Platform (GCP) credential, and it does need some GCP expertise. It will also put you on the exam on various topics, not only Google Cloud-related ones. This Machine Learning Engineer certification has the most non-Google information, in my opinion. It also has tremendous scalability beyond GCP if you ever need to operate in a multi- or hybrid-cloud environment.

How to pass the certification exam for Professional Machine Learning Engineers?

Aside from following their AI ideas and practices, Google has little bearing on the first portion of our test guide, which is all about presenting business challenges and data in a manner that ML can serve. You must also be able to identify scenarios in which ML is not appropriate.

Consider features like IAM and Key Management while creating your machine learning solution, which is more Google-y.

As a result, you must know when to employ virtual machines, containers, graphics processing units, and/or TPUs for training.

It’s also critical that you know how to use AI Platform’s built-in algorithms and features like Explainable AI and Continuous Evaluation.

It’s also critical to understand when completely autonomous pre-trained models, such as Speech-to-Text or Recommendations AI, are the best choice and when AutoML versions give the optimal combination of trade-offs. Because it’s a critical element of your job, the test asks you how you plan to implement your machine learning solutions.

Having mastered Cloud Dataprep and all of the Data Engineering tools we covered in the Professional Data Engineer cert is critical for the future, even if this course does not go into as much detail or focus as the Professional Data Engineer credential did. To be clear, this includes BigQuery, Cloud Dataflow, and Data Studio, among other tools of the trade.

Finally, you must put all of your Machine Learning ambitions into practice. This entails not only putting your design into action but also automating and monitoring many processes. There is no need for manual training in an ideal world, and a CI/CD system (MLOps) is in place to automatically test and release new models. For example, Cloud Build and Cloud Monitoring are both intriguing DevOps technologies.