Top IT Certifications of 2022: Google Cloud Professional Machine Learning Engineer

Top IT Certifications of 2022: Google Cloud Professional Machine Learning Engineer

Categories: AI & Machine Learning, Cloud Computing|Published On: January 10, 2022|6.2 min read|
About the Author

Syazana Khan

A communications specialist and technology wordsmith with over 2 years experience in the IT and professional development training arena.

Google Cloud Training and Certifications in 2022 

Most organizations around the world and across multiple industries are in the midst of digitally transforming their businesses. For many organizations and businesses, the Covid-19 has accelerated the speed of this paradigm shift. These digital transformations are already impacting the way we work and live, from enabling more sustainable, carbon neutral growth; to creating new business models shaped by artificial intelligence; to allowing unprecedented levels of virtual collaboration at global scale. 

The key driving force behind these shifts will be people with the skills required to implement and maintain large-scale cloud deployments, particularly in areas like artificial intelligence, machine learning, data analytics, application development, security, and cloud architecture. To ensure that the world has enough skilled people to execute these technologies at scale, we’re excited to announce a new goal of equipping more than 40 million people with Google Cloud skills. 


Google Cloud Certification Benefits


More than 90% of IT leaders say they’re looking to grow their cloud environments in the next several years, yet more than 80% of those same leaders identified a lack of skills and knowledge within their employees as a barrier to this growth. Through this new initiative, we aim to remove barriers for businesses and drive career success for individuals – via the cloud. 

Google Cloud Competitiveness

Google Cloud and Machine Learning 

Put the best of Google’s artificial intelligence to work helping your business run faster and smoother, while finding new ways to delight your customers. 

 Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution? 

Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on BigQuery, TensorFlow, Cloud Vision, Natural Language API (Application Programming Interfaces), and more. 


Professional Machine Learning Engineer 

A Data Scientist models and analyzes key data to continually improve how businesses utilize data. Data Scientists aim to make accurate predictions about the future using in-depth data modeling and deep learning. 


Google Cloud Professional Machine Learning Engineer Roadmap


A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance. 

The Professional Machine Learning Engineer exam assesses your ability to: 

  • Frame ML problems 
  • Architect ML solutions 
  • Design data preparation and processing systems 
  • Develop ML models 
  • Automate and orchestrate ML pipelines 
  • Monitor, optimize, and maintain ML solutions 



COURSE 1: GCPBD: Big Data & Machine Learning Fundamentals 

Get started with Google Cloud’s big data and machine learning products like BigQuery, Cloud SQL, Dataproc, and more. In this introductory course, you’ll learn how to best process data and create ML models for your needs. 


SKILL BADGE: Perform Foundational Data, ML, and AI Tasks in Google Cloud 

Get started with big data, machine learning, and artificial intelligence. Take your first steps with Google Cloud tools like BigQuery, Cloud Speech API, and AI Platform. You’ll have the opportunity to earn a Google Cloud skill badge upon completion. 


COURSE 2: GCP-MLGC: Machine Learning on Google Cloud 

In this course you will experiment with end-to-end machine learning on Google Cloud, starting from building a machine learning-focused strategy and progressing into model training, optimization, and productionalization. 

In this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform. 

Skills Covered 

  • Frame a business use case as a machine learning problem. 
  • Describe how to improve data quality. 
  • Perform exploratory data analysis. 
  • Build and train supervised learning models. 
  • Optimize and evaluate models using loss functions and performance metrics. 


COURSE 3: GCP-AMLTF: Advanced Machine Learning with TensorFlow on Google Cloud Platform 

This advanced course teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, and ends with building recommendation systems. This content can also be taken as part of the Advanced Solutions Lab. 

 This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems. 

Skills Covered 

  • Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. 
  • Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving. 
  • Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning. 
  • Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs. 
  • Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models. 


SKILL BADGE: Explore ML models with Explainable AI 

Get hands-on practice with Explainable AI – a set of tools and frameworks to help you develop interpretable and inclusive machine learning models and deploy them with confidence. Complete this quest, including the challenge lab at the end, to receive an exclusive Google Cloud digital badge. 


COURSE 4: GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals 

In this course you will learn MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. This content can also be taken as part of the Advanced Solutions Lab. 

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. 

Skills Covered 

  • Identify and use core technologies required to support effective MLOps. 
  • Configure and provision Google Cloud architectures for reliable and effective MLOps environments. 
  • Implement reliable and repeatable training and inference workflows. 
  • Adopt the best CI/CD practices in the context of ML systems. 
  • Operate deployed machine learning models effectively and efficiently. 


COURSE 5: GCP-MPGC: ML Pipelines on Google Cloud 

In this course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata. Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle. 

Skills Covered 

  • Orchestrate model training and deployment with TFX and Cloud AI Platform. ?Operate deployed machine learning models effectively and efficiently. ?Perform continuous training using various frameworks (Scikit Learn, XGBoost, PyTorch) and orchestrate pipelines using Cloud Composer and MLFlow. 
  • Integrate ML workflows with upstream and downstream data management workflows to maintain end-to-end lineage and metadata management. 


SKILL BADGE: Build and Deploy Machine Learning Solutions on Vertex AI 

Get practice with Vertex AI platform, AutoML, and custom training services. You’ll learn how to use Vertex AI for new and existing ML workloads, as well as how to leverage AutoML, custom training, and new MLOps services to significantly enhance development productivity and accelerate time to value. 

Accelerate your transformation with Google Cloud

Check out our latest Google Cloud Machine Learning timetable:  

Code Course Title Days Fees (RM) July Aug Sept
GCPBD Big Data & Machine Learning Fundamentals 1 RM2,400 25 15 26
GCP-MLGC Machine Learning on Google Cloud 5 RM2,400 15-19
GCP-AMLTF Advanced Machine Learning with TensorFlow on Google Cloud Platform 5 RM14000
GCP-MLOF MLOps (Machine Learning Operations) Fundamentals 1 RM2,400 1
GCP-MPGC ML Pipelines on Google Cloud 1 RM2,400 2


Get started with Google Cloud Machine Learning Engineer certification

About the Author

Syazana Khan

A communications specialist and technology wordsmith with over 2 years experience in the IT and professional development training arena.