GCP200MLEXAMPREP
Preparing for Professional Machine Learning Engineer
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.
What you will 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.
Prerequisites
- Some familiarity with basic machine learning concepts
- Experience with Google Cloud products such as Vertex AI, BigQuery, and Cloud Storage
Target audience
- Googlers, partners, and customers preparing for the Professional Machine Learning Engineer certification exam
Training Program
8 modules to master the fundamentals
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
Topics covered
- →Course agenda
- →The value of Google PMLE certification
- →The role of a PMLE
- →About the Cymbal Retail (fictional company used in the course)
- →Resources to support your certification journey
- →Creating a study plan
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.
Topics covered
- →Understand customer segments using BigQuery and a clustering model.
- →Predict customer value using AutoML on a customer dataset.
- →Build a conversational AI assistant for customers using Vertex AI Agent Builder and retrieval-augmented generation (RAG).
- →Diagnostic questions
- →Review and study planning
Activities
Lecture
Diagnostic questions
Quiz
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.
Topics covered
- →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 (customer churn).
- →Answer diagnostic questions.
- →Review the information and plan your study.
Activities
Lecture
Diagnostic questions
Quiz
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.
Topics covered
- →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.
Activities
Lecture
Diagnostic questions
Quiz
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.
Topics covered
- →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.
Activities
Lecture
Diagnostic questions
Quiz
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).
Topics covered
- →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.
Activities
Lecture
Diagnostic questions
Quiz
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.
Topics covered
- →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.
Activities
Lecture
Diagnostic questions
Quiz
Objectives
- Review a sample study plan for the exam
- Learn how to register for the exam
Topics covered
- →A sample study plan for the exam
- →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
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote