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Axalon Training
T-GCPMLE-AOfficial Google Curriculum

Preparing for Professional Machine Learning Engineer

1 dayILTIntermediateLoading...

Overview

Course helps learners create a study plan for the PMLE certification exam, exploring breadth and scope of domains covered in the exam, assessing exam readiness and creating individual study plans.

What You'll Learn

  • 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

Who Should Attend

Googlers, partners, and customers

Prerequisites

None

Products Covered

Vertex AIAutoMLBigQueryCloud StorageCloud SQLCloud SpannerDataflowDataprocCloud ComposerCloud BuildIdentity and Access Management

Course Modules

1

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

Learning Outcomes

  • 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
2

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
3

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
4

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
5

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
6

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
7

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

Learning Outcomes

  • 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

LectureDiagnostic questionsQuiz
8

Your next steps

Topics

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

Learning Outcomes

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

Activities

Create your study plan for the examIdentify a date to take the exam based upon your planRegister for the exam

What's Not Covered

  • Other Google Cloud products not included in the list

Get This Training

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Course Details

Course Code
T-GCPMLE-A
Duration
1 day
Format
ILT
Level
Intermediate
Modules
8
Activities
12
Price
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