Google Cloud Machine Learning Engineer Certification Prep

Building, Deploying, and Managing Machine Learning Services at Scale

Created by: Dan Sullivan
Cloud Architect, Data Engineer and Author

What Will I 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

Requirements

• Familiarity with basic cloud concepts
• Understanding of some use cases of machine learning

Target audience

• ML Engineers who wish to pass the Google Cloud Professional Machine Learning certification exam.
• Beginner machine learning engineers wanting to understand MLOps
• Software developers who want to use ML services to use ML as an alternative to coding solutions
• Cloud architects who want to understand how to design for machine learning serivces
• Data engineers who want to expand their skillset to include machine learning operations
• Data analysts and data scientists who want to use machine learning in their work.