Preparing for Professional Machine Learning Engineer (GC-CERT-MLE)

This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam.

Learners explore the breadth and scope of the domains covered in the exam.

Learners assess their exam readiness and create their individual study plan.

• List the domains covered on the Professional Machine Learning Engineer (PMLE) certification exam.
• Identify gaps in your knowledge and skills for each domain.
• Identify resources and learning assets available to develop your knowledge and skills.
• Create a study plan to prepare for the PMLE certification exam

Googlers, partners, and customers.

Module 0: Introduction

• Course agenda
• Module agenda
• The value of Google PMLE certification
• The role of an PMLE
• About the Cymbal Retail (fictional company used in the course)
• Resources to support your certification journey
• Creating a study plan

Module 1: Architecting low-code AI solutions

• Ira needs to understand customer segments using BigQuery and a clustering model.
• Sasha needs to predict customer value using AutoML Cymbal Retail’s customer dataset.
• Taylor needs to build a conversational AI assistant for customers using Vertex AI Agent Builder and retrieval-augmented generation (RAG)
• Diagnostic questions
• Review and study planning

Module 2: Collaborating within and across teams to manage data and models

• Use Google Cloud's products and Cymbal Retail's rich data to design a model to predict which high-value customers are likely to stop purchasing (also known as customer churn).
• Answer diagnostic questions.
• Review the information and plan your study.

Module 3: Scaling prototypes into ML models

• Use Google Cloud's products and Cymbal Retail's rich data to build and scale customer churn prototype into a production-ready model
• Answer diagnostic questions.
• Review the information and plan your study

Module 4: Serving ML models

• Use Google Cloud's products and Cymbal Retail's rich data to deploy a customer churn model and use it in production for inference.
• Answer diagnostic questions.
• Review the information and plan your study.

Module 5: Automating and orchestrating ML pipelines

• Use Google Cloud’s products to orchestrate the entire machine learning pipeline for seamless execution and continuous improvement with customer churn.
• Answer diagnostic questions.
• Review the information and plan your study.

Module 6: Monitoring ML Solutions

• Use Google Cloud’s products to ensure the customer churn model remains robust, reliable, and aligned with Google’s Responsible AI principles.
• Answer diagnostic questions.
• Review the information and plan your study.

Module 7: Your next steps

• A sample study plan for the exam
• How to register for the exam