Preparing for Professional Machine Learning Engineer (PPMLE)

This course is part of the following Certifications:

• Google Cloud Certified Professional Machine Learning Engineer

Module 0 - Introduction

Topics

• 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

Objectives

• Explain the value of the Google PMLE certification
• Describe the role of a Professional Machine Learning Engineer
• Explain what Cymbal Retail is, and how the company will be used throughout the course.
• Identify resources to support your certification journey

Module 1 - Architecting low-code AI solutions

Topics

• 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

Objectives

• Identify your level of knowledge in developing and implementing BigQuery ML and AutoML machine learning solutions.
• Determine the skills needed to select appropriate ML APIs, prepare data effectively, and build custom models using AutoML.

Activities

• Lecture
• Diagnostic questions
• Quiz

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

Topics

• 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.

Objectives

• Identify your level of knowledge in exploring, preprocessing, and managing organization-wide data.
• Identify your level of knowledge in addressing privacy implications and leveraging tools like Vertex AI Feature Store.
• Determine the skills needed to prototype models using Jupyter notebooks on Google Cloud.
• Determine the skills needed to select appropriate backends, implement security best practices, and integrate with code repositories.

Activities

• Lecture
• Diagnostic questions
• Quiz

Module 3 - Scaling prototypes into ML models

Topics

• 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.

Objectives

• Identify your level of knowledge in scaling ML prototypes into production-ready models
• Identify your level of knowledge in selecting appropriate ML frameworks, model architectures, and modeling techniques based on interpretability requirements.
• Determine the skills needed to train models effectively, including organizing and ingesting training data on Google Cloud.
• Determine the skill needed to utilize distributed training techniques, perform hyperparameter tuning, and troubleshoot training failures.

Activities

• Lecture
• Diagnostic questions
• Quiz

Module 4 - Serving ML models

Topics

• 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

Objectives

• Identify the level of knowledge needed to effectively serve models in production.
• Identify the level of knowledge needed to select between batch and online inference, utilize various serving frameworks, organize a model registry, and conduct A/B testing for model optimization.
• Determine the skills needed to scale online model serving, including leveraging Vertex AI Feature Store.
• Determine the skills needed to manage public and private endpoints, choose appropriate hardware, optimize serving backends for throughput, and fine-tune models for optimal performance in production.

Activities

• Lecture
• Diagnostic questions
• Quiz

Module 5 - Automating and orchestrating ML pipelines

Topics

• 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.

Objectives

• Identify the level of knowledge needed to develop and maintain end-to-end ML pipelines.
• Identify the level of knowledge needed to validate data and model, consistent preprocessing, hosting options, component identification, parameterization, triggering mechanisms, compute needs, orchestration strategies.
• Determine the skills needed to automate model retraining, including establishing retraining policies.
• Determine the skills needed to implement CI/CD model deployment, and track and audit metadata (model artifacts, versions, data lineage).

Activities

• Lecture
• Diagnostic questions
• Quiz

Module 6 - Monitoring ML Solutions

Topics

• 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.

Objectives

• Identify the level of knowledge needed to assess and mitigate risks in ML solutions.
• Identify the level of knowledge needed to build secure ML systems, align with responsible AI practices, evaluate solution readiness, and utilize model explainability on Vertex AI.
• Determine the skills needed to monitor, test, and troubleshoot ML solutions.
• Determine the skills needed to establish continuous evaluation metrics, monitor for training-serving skew and feature drift, compare model performance against baselines, and investigate common training and serving errors.

Activities

• Lecture
• Diagnostic questions
• Quiz

Module 7 - Your next steps

Topics

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

Objectives

• Review a sample study plan for the exam
• Learn how to register for the exam

Activities

• Create your study plan for the exam
• Identify a date to take the exam based upon your plan
• Register for the exam