Google Cloud Machine Learning Engineer Certification Prep
Building, Deploying, and Managing Machine Learning Services at Scale
Created by Dan Sullivan | 4.5 hours on-demand video course
Machine Learning Engineer is a rewarding, in demand role, and increasingly important to organizations moving building data intensive services in the cloud. The Google Cloud Professional Machine Learning Engineer certification is one of the field’s most recognized credentials. This course will help prepare you to take and pass the exam. By the end of this course, you will know how to use Google Cloud services for machine learning and just as importantly, you will understand machine learning concepts and techniques needed to use those services effectively.
Unlike courses that set out to teach you how to use particular Google Cloud services, this course is designed to teach you services as well as all the topics covered in the Google Cloud Professional Machine Learning Exam Guide, including machine learning fundamentals and techniques.
What you’ll learn
- Understand how to use Google Cloud services to build, deploy, and manage machine learning models in production
- Use Vertex AI, BigQuery, Cloud Dataflow, and Cloud Dataproc in ML pipelines
- Tune training and serving pipelines
- Choose appropriate infrastructure, including virtual machines, containers, GPUs and TPUS
- How to secure data in ML operations while protecting privacy
- Monitor machine learning models in production and know when to retrain models
- Explore datasets to identify problems and resolve issues such as class imbalance and insufficient data
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