Professional Machine Learning Engineer

Validate cloud professionals’ understanding in designing, building, and productionizing machine-learning models to solve business challenges using Google Cloud technologies, along with their knowledge of proven ML models and techniques.

32 hours 0 Enrolled No ratings yet Intermediate

About The Course

A Professional Machine Learning Engineer designs builds, and productions ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process and collaborates closely with other job roles to ensure the long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.

The Professional Machine Learning Engineer exam assesses your ability to:

  • Frame ML problems
  • Architect ML solutions
  • Design data preparation and processing systems
  • Develop ML models
  • Automate & orchestrate ML pipelines
  • Monitor, optimize, and maintain ML solutions

Course Objectives

  • Frame machine learning problems
  • Design a machine learning solution architecture
  • Prepare and process data
  • Develop machine learning models
  • Automate and orchestrate machine learning pipelines
  • Monitor, optimize, and maintain machine learning solutions

Recommended Experience

3+ years of industry experience including 1+ years designing & managing solutions using Google Cloud.

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What's included

  • 32 Hours Training Course
  • Certificate
  • 6 Module
  • 24/7 Support


  • Engineers who successfully wish to work on google workshop’s powerful & efficient tools
  • AI Engineers/ML Engineers
  • AI Developers
  • System/Software Engineers


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