Why become a Professional Machine Learning Engineer with Google Cloud?


Data is the present and allows us to design the future. Companies know this and many are already making great efforts to analyze and interpret them in order to make decisions that will make the difference between keeping up with the pace of the market and its demands, or falling behind.

The machine learning, one of the disciplines of Artificial Intelligence, is already a reality among us. It is present in endless applications and operations of our day to day and differs from other techniques for its ability to adapt to changes in data as they enter the system.

The presence of this technology is increasing and, also, fundamental in how organizations are managed; being essential, for any self-respecting data scientist or engineer, have up-to-date expertise on the subject.

The world of data needs specialists

As we said at the beginning, companies know the importance of this discipline, and a high percentage of them already use Machine Learning (ML) in their processes and decision making.

However, Finding job profiles with the right ML skills is a top concern for IT leaders. In fact, the demand for digital professionals in Europe has experienced a growth of 41.3% compared to 3.4% for the rest of the demand for professionals, according to the report ‘Digital Talent Overview 2020’

We are not talking about the future, but about the present, and in this article we are going to explain how, thanks to the Google Cloud Professional Machine Learning Engineer certification, you can prepare for the challenges of the data era.

Get certified with Google Cloud and boost your skills

A Professional Machine Learning Engineer is responsible for designing, building, and producing machine learning models to solve business challenges using Google Cloud technologies and knowledge of proven models and techniques. The ML engineer is a proficient in all aspects of model architecture, data pipeline interaction, and metric interpretation, and needs familiarity with application development, infrastructure management, data engineering, and security.

This certification demonstrates that the candidate has the necessary knowledge to perform the following tasks:

  • Framing ML issues
  • Design ML solutions
  • Prepare and process data
  • Develop ML models
  • Automate and orchestrate ML pipelines
  • Monitor, optimize and maintain ML solutions

Experience machine learning from start to finish

Google Cloud has courses, itineraries and learning plans aimed at preparing for its different certifications. On PUE, Google Cloud Service & Training Partner and official Kryterion certification centerwe are at your disposal to address any Google Cloud project, course or certification in which you are interested.

Courses that prepare for certification Professional Machine Learning Engineer are the following:

This certification includes a training itinerary that PUE makes available to you with advantageous conditions for professionals and companies interested in this training. At the moment All our official Google Cloud courses have a 20% discount. Until March 31st.

How should I prepare myself for the exam?

Exam duration: 2 hours

Language: English

Format: Multiple choice questions with one or more answers

Previous requirements: It is advisable to carry out the training mentioned above in this post

recommended experience: 3+ years of industry experience and 1+ year of experience designing and managing solutions with GCP

Additional Resources:

You can consult the official exam guide here.

You can take a sample questionnaire here.

Take the exam online through the option Kryterion’s online-proctoring (OLP) solution.

Links of interest

Official Google Cloud Training and Certification

Our professional services from successful migration to cloud computing


training@pue.es for training official on Google Cloud.

exams@pue.es for certification official on Google Cloud.

consulting@pue.es for services professionals in Google Cloud technologies.


Related Posts

Leave a Reply

Your email address will not be published.