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Daniel Stern

In this course, Google Cloud Certified Professional Machine Learning Engineer, you’ll learn to develop, manage, maintain and utilize Google Cloud machine learning tools and workflows, and gain the knowledge needed to pass the Google Cloud Certified Professional Machine Learning Engineer exam. First, you’ll learn how to Frame Machine Learning Problems. You’ll have the skills necessary to choose the best ML solution for a task, to define ML problems, to understand ML success criteria, and identify risks to feasibility of ML solutions. Next, you’ll gain the skills needed to Architect ML solutions. You’ll be able to design effective ML solutions, choose hardware components, and design security compliant architecture. After that, you’ll learn how to design data preparation and processing systems. This will give you the skills to explore data with statistics and visualizations, build data pipelines, and engineer features. Following that, you’ll discover ML Model Development. You’ll learn to build models, train models, test models and scale them. With the knowledge you gain in the above sections, you’ll be ready to learn how to automate and orchestrate ML pipelines. This includes designing training pipelines, impleming serving pipelines, and tracking and auditing metadata. Finally, you’ll learn to optimize, monitor and maintain ML solutions. You’ll learn to monitor and troubleshoot ML solutions,as well as tune them to attain optimum effectiveness. When you’re finished with this course, you’ll have the skills and knowledge of Machine Learning on GCP needed to pass the GCP Professional ML Engineer Exam and to engage in meaningful ML development on GCP in the real world.

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Prepares learners for the Google Cloud Certified Professional Machine Learning Engineer Exam
Taught by Daniel Stern, an expert in Machine Learning
Provides a comprehensive overview of Machine Learning on GCP
Covers essential Machine Learning concepts and techniques
Suitable for learners with a strong foundation in Machine Learning
Requires a basic understanding of Cloud Computing

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Google Cloud Certified Professional Machine Learning Engineer with these activities:
Review Fundamentals of Machine Learning
Strengthens your understanding of the core concepts in Machine Learning.
Browse courses on Machine Learning Basics
Show steps
  • Review course notes and textbooks covering the fundamentals of ML.
  • Complete practice exercises and quizzes to test your knowledge.
  • Watch online videos or attend webinars on ML basics.
Data Types Review
Reinforces your understanding of building data pipelines and developing models.
Browse courses on Data Types
Show steps
  • Solve coding exercises reviewing data types and structures.
  • Practice debugging and fixing code related to data types.
  • Participate in online coding challenges focusing on data types.
Create a Machine Learning Glossary
Enhances your understanding of key concepts through active recall.
Show steps
  • Compile a list of important ML terms and definitions.
  • Create flashcards or a digital glossary using tools like Anki or Notion.
  • Regularly review and test your understanding of the terms.
Five other activities
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Build a simple ML model with TensorFlow
Provides you with hands-on experience applying the concepts learned in the course.
Browse courses on Model Development
Show steps
  • Follow a guided tutorial on how to build a simple ML model using TensorFlow.
  • Modify the model to fit your own dataset and problem statement.
  • Deploy your model to a cloud platform.
Participate in Hands-on ML Workshops
Provides practical experience working on ML projects.
Browse courses on Machine Learning Projects
Show steps
  • Find workshops that align with your interests and skill level.
  • Participate actively in hands-on exercises and discussions.
  • Build a portfolio of projects to showcase your skills.
Discuss ML case studies with peers
Fosters your critical thinking and problem-solving skills.
Show steps
  • Join an online or in-person study group to discuss ML case studies.
  • Analyze case studies and identify the challenges and solutions implemented.
  • Present your findings to the group and engage in discussions.
Attend ML Meetups and Conferences
Connects you with professionals in the field and keeps you updated on industry trends.
Browse courses on Networking
Show steps
  • Identify local or virtual ML meetups and conferences.
  • Attend events regularly to network and learn from others.
  • Follow industry leaders and experts on social media.
Contribute to Open-Source ML Projects
Enhances your understanding of ML best practices and industry standards.
Browse courses on Open Source
Show steps
  • Identify open-source ML projects that interest you.
  • Read the project documentation and codebase.
  • Start contributing by fixing bugs, adding features, or improving documentation.
  • Collaborate with other contributors and learn from their expertise.

Career center

Learners who complete Google Cloud Certified Professional Machine Learning Engineer will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine learning Engineers design, develop, deploy, maintain, and test machine learning algorithms to solve complex problems. The Google Cloud Certified Professional Machine Learning Engineer course teaches the skills necessary to excel in these roles. The course also covers designing effective ML solutions, building ML pipelines, and monitoring and maintaining ML solutions.
Data Scientist
Data Scientists build effective ML solutions, which aligns with the skills learned in this Google Cloud Certified Professional Machine Learning Engineer course. Beyond this, designing ML pipelines and ML workflow automation require data science knowledge. Studying this course provides a solid foundation for your data science career due to its emphasis on GCP tools and workflows.
Data Engineer
Data Engineers build and maintain the infrastructure that is used to store and process data. A strong understanding of ML is essential for Data Engineers who want to work with ML data. This course provides a solid foundation in ML on GCP, enabling Data Engineers to build robust, scalable, and efficient ML systems.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make predictions about the future. A strong understanding of ML can help Quantitative Analysts to build more accurate and reliable models. This course provides a solid foundation in ML on GCP, enabling Quantitative Analysts to make informed decisions about how to use ML in their work.
Researcher
Researchers who work in the field of ML can benefit from taking the Google Cloud Certified Professional Machine Learning Engineer course. This course covers the fundamentals of ML on GCP, including how to design, develop, and deploy ML solutions. With this knowledge, Researchers can gain a deeper understanding of ML and its applications.
Artificial Intelligence Engineer
AI Engineers work on a range of tasks, including designing, developing, and testing AI systems. To succeed, they must have a strong understanding of ML. This Google Cloud Certified Professional Machine Learning Engineer course covers the fundamentals of ML on GCP, enabling you to build a strong foundation for an AI Engineering career.
Data Analyst
Data Analysts use data to solve business problems. This course can help by teaching you the skills necessary to identify risks to feasibility of ML solutions and to monitor and troubleshoot ML solutions. These skills are essential for any Data Analyst who wants to use ML to improve their work.
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. A strong understanding of ML is increasingly important for Cloud Architects, as more and more organizations are using ML to improve their operations. This Google Cloud Certified Professional Machine Learning Engineer course provides a solid foundation in ML on GCP, enabling Cloud Architects to make informed decisions about how to use ML in their designs.
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. Those who specialize in ML can use this Google Cloud Certified Professional Machine Learning Engineer course to learn how to design, build, and deploy ML solutions. This can help them to improve the efficiency and reliability of their ML pipelines.
Trainer
Trainers teach others about new technologies and trends. A strong understanding of ML can help Trainers to develop more effective training programs. This course provides a solid foundation in ML on GCP, enabling Trainers to make informed decisions about how to use ML in their work.
Software Engineer
Software Engineers who want to specialize in ML can benefit from taking the Google Cloud Certified Professional Machine Learning Engineer course. This course covers the fundamentals of ML on GCP, including how to design, develop, and deploy ML solutions. With this knowledge, Software Engineers can build robust, scalable, and efficient ML systems.
Product Manager
Product Managers are responsible for the development and launch of products. A strong understanding of ML can help Product Managers to make better decisions about how to use ML in their products. This course provides a solid foundation in ML on GCP, enabling Product Managers to make informed decisions about how to use ML to improve their products.
Business Analyst
Business Analysts use data to help businesses make better decisions. A strong understanding of ML can help Business Analysts to identify opportunities to use ML to improve their work. This course provides a solid foundation in ML on GCP, enabling Business Analysts to make informed decisions about how to use ML in their work.
Consultant
Consultants help organizations to improve their performance. A strong understanding of ML can help Consultants to identify opportunities to use ML to improve their work. This course provides a solid foundation in ML on GCP, enabling Consultants to make informed decisions about how to use ML in their work.
Writer
Writers who specialize in technology can use this Google Cloud Certified Professional Machine Learning Engineer course to learn about the latest trends in ML. This can help them to write more informed and engaging articles and blog posts about ML. While ML is not a strict requirement for this type of role, this course may be useful for writers who wish to specialize in ML.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Google Cloud Certified Professional Machine Learning Engineer.
Provides a practical guide to using Google Cloud Platform (GCP) for machine learning tasks. It covers a wide range of topics, from data preparation and model training to model deployment and monitoring. It good choice for those who want to learn more about using GCP for machine learning.
Provides a comprehensive overview of statistical learning concepts and algorithms. It good choice for those who want to learn more about the theory and practice of statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning concepts and algorithms. It good choice for those who want to learn more about the theory and practice of pattern recognition and machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It good choice for those who want to learn more about the theory and practice of machine learning from a probabilistic perspective.
Provides a comprehensive overview of natural language processing (NLP) concepts and algorithms. It good choice for those who want to learn more about the theory and practice of NLP.
Provides a comprehensive overview of computer vision concepts and algorithms. It good choice for those who want to learn more about the theory and practice of computer vision.
Provides a comprehensive overview of speech and language processing (SLP) concepts and algorithms. It good choice for those who want to learn more about the theory and practice of SLP.
Provides a comprehensive overview of reinforcement learning concepts and algorithms. It good choice for those who want to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of generative adversarial networks (GANs) concepts and algorithms. It good choice for those who want to learn more about the theory and practice of GANs.
Provides a comprehensive overview of information theory, inference, and learning algorithms concepts and algorithms. It good choice for those who want to learn more about the theory and practice of information theory, inference, and learning algorithms.
Provides a comprehensive overview of deep learning concepts and algorithms. It good choice for those who want to learn more about deep learning theory and practice.
Provides a practical guide to machine learning for those with a programming background. It covers a wide range of topics, from data preparation and model training to model deployment and monitoring. It good choice for those who want to learn more about machine learning in a hands-on way.

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