Preparing for Professional Machine Learning Engineer

Introduction

• 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

Architecting low-code AI solutions

• 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.

Collaborating within and across teams to manage data and models

• 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.

Scaling prototypes into ML models

• 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.

Serving ML models

• 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.

Automating and orchestrating ML pipelines

• 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).

Monitoring ML Solutions

• 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.

Your next steps

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