Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Course Syllabus

Overview: This professional certificate program is designed to prepare learners for the Google Cloud Professional Machine Learning Engineer certification. The course spans approximately 60 hours of content, delivered through a flexible, self-paced structure. Learners will gain hands-on experience using Google Cloud technologies, focusing on designing, building, and productionalizing machine learning models. Through a series of modules and labs using Qwiklabs, participants will develop practical skills essential for real-world ML engineering roles.

Module 1: Introduction to AI and Machine Learning on Google Cloud

Estimated time: 9 hours

• Explore Google’s AI and ML ecosystem
• Understand the role of a Machine Learning Engineer
• Get started with Vertex AI
• Overview of the Professional Machine Learning Engineer certification

Module 2: Build, Train, and Deploy ML Models with Keras on Google Cloud

Estimated time: 13 hours

• Design TensorFlow input data pipelines
• Train models using Keras and TensorFlow
• Deploy ML models at scale with Vertex AI
• Use managed services for model training and prediction

Module 3: Feature Engineering

Estimated time: 8 hours

• Perform feature engineering with BigQuery ML
• Process features using Dataflow and Dataprep
• Apply transformations in Keras and TensorFlow

Module 4: Machine Learning in the Enterprise

Estimated time: 8 hours

• Identify core technologies for MLOps
• Implement reliable training workflows
• Design scalable inference pipelines

Module 5: Production Machine Learning Systems

Estimated time: 9 hours

• Implement batch and real-time ML systems
• Analyze real-world ML case studies
• Optimize models for production environments

Module 6: MLOps Fundamentals

Estimated time: 13 hours

• Deploy ML models using MLOps tools
• Evaluate and monitor model performance
• Manage and version models in production
• Apply best practices for continuous integration and delivery

Prerequisites

• Familiarity with Python programming
• Understanding of basic machine learning concepts
• Basic knowledge of Google Cloud Platform

What You'll Be Able to Do After

• Design and build ML models using Vertex AI
• Engineer features effectively using BigQuery ML and Dataflow
• Deploy and manage ML models in production environments
• Implement MLOps practices for monitoring and model governance
• Prepare for the Google Cloud Professional Machine Learning Engineer certification exam