Google Cloud Certification with GenAI

Overview

Master Google Cloud ML engineering skills and prepare for Professional Machine Learning Engineer certification through hands-on labs and comprehensive training.

Syllabus

• Introduction to Google Cloud Machine Learning
Overview of Google Cloud Platform (GCP) services
Introduction to Machine Learning on GCP
Understanding GenAI concepts and applications
• Setting Up Google Cloud Environment
Creating and managing projects in GCP
Understanding billing and resource management
Using Google Cloud Console and Cloud SDK
• Designing and Building Machine Learning Models
Introduction to TensorFlow and its integration with GCP
Data preparation and feature engineering on GCP
Building and training ML models using AI Platform
• Deploying and Managing ML Models
Model deployment on AI Platform
Versioning and managing models
Monitoring and logging ML models in production
• Automating and Orchestrating ML Pipelines
Introduction to Kubeflow Pipelines on GCP
Building reusable ML pipelines
CI/CD best practices for ML models
• Advanced Topics in Google Cloud AI
Understanding GenAI capabilities in GCP
Using Vertex AI for advanced model management
Exploring AutoML and BigQuery ML
• Data Security and Compliance
Best practices for data security and privacy on GCP
Understanding compliance requirements
• Preparing for the Google Cloud Professional Machine Learning Engineer Certification
Exam structure and key knowledge areas
Study tips and resources
Practice questions and scenarios
• Hands-On Labs and Projects
Real-world scenarios using GCP services for ML
Guided labs to reinforce learning concepts
Capstone project to demonstrate full cycle ML engineering
• Course Review and Resources
Summary of key learning objectives
Additional resources and next steps in AI specialization

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Subjects

Programming