What you’ll learn

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.