PMLE - Professional Machine Learning Engineer Study Guide 2026

Key Competencies (3)

• 1

  Developing ML models by using BigQuery ML

  Ability to create and implement machine learning models using BigQuery's integrated ML capabilities.

• 2

  Building AI solutions by using ML APIs

  Leveraging pre-trained Google Cloud AI APIs for common ML tasks without custom model development.

• 3

  Training models by using AutoML

  Utilizing Google Cloud's AutoML services to train custom models with minimal coding.

Key Competencies (5)

• 1

  Exploring and preprocessing organization-wide data

  Discovering, accessing, and preparing data from various organizational sources for ML workflows.

• 2

  Managing a data science workspace

  Setting up and maintaining collaborative environments for data science teams.

• 3

  Managing features and datasets for ML

  Implementing feature stores and dataset management systems for ML operations.

• 4

  Managing ML models and versions

  Implementing model lifecycle management and version control systems.

• 5

  Managing and tracking ML experiments

  Implementing experiment tracking and management systems for ML research and development.

Key Competencies (4)

• 1

  Developing training and serving infrastructure

  Designing and implementing scalable infrastructure for ML training and inference workloads.

• 2

  Implementing training pipelines

  Building automated, scalable pipelines for ML model training and retraining.

• 3

  Implementing serving pipelines

  Developing production-ready model serving infrastructure and APIs.

• 4

  Implementing ML workflow orchestration

  Orchestrating complex ML workflows using pipeline automation tools.

Key Competencies (4)

• 1

  Troubleshooting ML solutions and models

  Diagnosing and resolving issues in production ML systems.

• 2

  Testing for target performance

  Implementing comprehensive testing strategies to ensure ML models meet performance requirements.

• 3

  Tuning performance of ML solutions

  Optimizing ML systems for improved performance, cost, and efficiency.

• 4

  Building an ML solution monitoring strategy

  Implementing comprehensive monitoring for ML systems in production.

Key Competencies (4)

• 1

  Orchestrating ML training and inference workflows

  Coordinating complex ML training and serving operations across distributed systems.

• 2

  Designing and implementing ML workflows

  Architecting efficient and maintainable ML workflow systems.

• 3

  Implementing ML pipeline automation

  Implementing automation strategies to reduce manual intervention in ML workflows.

• 4

  Developing end-to-end ML pipelines

  Creating comprehensive automated pipelines that span the entire ML lifecycle.

Key Competencies (4)

• 1

  Ensuring ML solution reliability

  Implementing reliability engineering practices for ML systems.

• 2

  Maintaining ML solutions

  Implementing maintenance strategies for long-term reliability.

• 3

  Tuning performance and optimizing ML solutions for cost and efficiency

  Optimizing for improved performance and reduced costs.

• 4

  Monitoring and troubleshooting model and infrastructure performance

  Implementing monitoring and diagnostic tools for ML systems.