Google Cloud Machine Learning Engineer Certification Prep
Google Cloud Machine Learning Engineer Certification Prep
Building, Deploying, and Managing Machine Learning Services at Scale
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Created by Dan Sullivan | 4.5 hours on-demand video course
Google Cloud Machine Learning Engineer Certification Course Overview
Online Course: Google Cloud Machine Learning Engineer Certification Prep from Udemy
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. Specifically, this course will help you understand the details of:
- Building and deploying ML models to solve business challenges using Google Cloud services and best practices for machine learning
- Aspects of machine learning model architecture, data pipelines structures, optimization, as well as monitoring model performance in production
- Fundamental concepts of model development, infrastructure management, data engineering, and data governance
- Preparing data, optimizing storage formats, performing exploratory data analysis, and handling missing data
- Feature engineering, data augmentation, and feature encoding to maximize the likelihood of building successful models
- Understand responsible AI throughout the ML development process and apply proper controls and governance to ensure fairness in machine learning models.
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
Best Google Cloud Professional Machine Learning Engineer Course On Udemy (2024)
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Who this course is for:
- 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.
Taught by Dan Sullivan
