Professional Machine Learning Engineer Prep:
The Professional Machine Learning Engineer (PMLE) certification tests your ability to build, deploy, and monitor ML solutions using Gemini Enterprise Agent Platform, BigQuery ML, and Model Garden. This guide maps every domain and objective to verified Google Cloud documentation.
You can also explore more GCP certification study guides on the GCP category page to keep building your skills.
Google Machine Learning Engineer Materials:
| Coursera | Google Cloud Machine Learning Engineer Professional Certificate |
| Udemy | Google Cloud Professional Machine Learning Engineer |
| Whizlabs | Certified Professional Machine Learning Engineer |
Section 1: Architecting low-code AI solutions (~13% of the exam)
1.1 Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform. Considerations include:
Building models in BigQuery ML or Agent Platform AutoML (e.g., classification, regression, forecasting, and clustering) based on the business problem
Introduction to ML in BigQuery
Gemini Enterprise Agent Platform for BigQuery users
Performing feature engineering or selection using BigQuery ML
Feature preprocessing overview
Perform feature engineering with the TRANSFORM clause
Generating predictions using BigQuery ML
Introduction to ML in BigQuery
BigQuery ML and Model Registry
Perform feature engineering with the TRANSFORM clause
Training models using Agent Platform AutoML
Fine-tuning Gemini models using BigQuery
The CREATE MODEL statement for fine-tuning Vertex AI Gemini models
The CREATE MODEL statement for Gemini Enterprise Agent Platform LLMs as MaaS
1.2 Building AI solutions using Google Cloud AI APIs or foundational models. Considerations include:
Evaluating and selecting the appropriate model for a given task from Gemini Enterprise Agent Platform Model Garden
Overview of models on Agent Platform
Building applications using industry-specific APIs (e.g., Document AI API, Vision API, and Translate API)
Cloud Vision API documentation
Overview of the Cloud Translation API
Building solutions and tuning models for specific use cases (e.g., Gemini, Imagen, Veo, and models as a service in Model Garden)
Generate videos with Veo on Gemini Enterprise Agent Platform
Optimizing Gemini-based applications for cost, latency, and availability
Section 2: Collaborating within and across teams to manage data and models (~16% of the exam)
2.1 Exploring and preprocessing data for ML. Considerations include:
Organizing and exploring different data types (e.g., tabular, text, and images) for efficient experimenting, training, and serving
About Feature Store on Gemini Enterprise Agent Platform
Choosing the right tool for data preprocessing based on scale and complexity (e.g., BigQuery [SQL], Dataflow, Apache Spark, and in-memory Python frameworks)
Introduction to ML in BigQuery
Managed Service for Apache Spark documentation
Creating and consolidating features in Gemini Enterprise Agent Platform Feature Store
About Feature Store on Gemini Enterprise Agent Platform
Introduction to feature management in Gemini Enterprise Agent Platform
Ensuring data privacy and handling sensitive information (e.g., personally identifiable information [PII])
Sensitive Data Protection overview
Classification, redaction, and de-identification
2.2 Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and Colab Enterprise). Considerations include:
Applying collaboration and security best practices when setting up and running notebook environments
Introduction to Agent Platform Workbench
Developing models in Agent Platform Workbench or Colab Enterprise notebooks using common frameworks (e.g., PyTorch, sklearn, and JAX)
Introduction to Agent Platform Workbench
Using a variety of foundational and open-source models in Model Garden to create model prototypes in notebook environments
Overview of self-deployed models
2.3 Tracking and running ML experiments. Considerations include:
Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Experiments on Gemini Enterprise Agent Platform, Gemini Enterprise Agent Platform Pipelines, and Kubeflow Pipelines) given the framework
Introduction to Gemini Enterprise Agent Platform Experiments
Introduction to Gemini Enterprise Agent Platform Pipelines
Evaluating predictive and gen AI solutions (e.g., model evaluation metrics and LLM-as-a-judge)
Gen AI evaluation service overview
Tracking and comparing model artifacts, versions, and lineage (e.g., Experiments on Agent Platform and Gemini Enterprise Agent Platform ML Metadata)
Introduction to Gemini Enterprise Agent Platform Experiments
Introduction to Gemini Enterprise Agent Platform ML Metadata
Section 3: Scaling prototypes into ML models (~21% of the exam)
3.1 Building models given the task considering cost, complexity, latency, and scalability. Considerations include:
Choosing the model type (e.g., ARIMA, DNN, and LLM)
Introduction to ML in BigQuery
Tabular Workflows on Agent Platform
Choosing the product (e.g., Agent Platform AutoML, BigQuery ML, and Agent Platform Pipelines)
Introduction to ML in BigQuery
Introduction to Gemini Enterprise Agent Platform Pipelines
Choosing the deployment strategy
Modeling techniques given interpretability requirements
Introduction to Vertex Explainable AI
Introduction to Model Monitoring
3.2 Training models. Considerations include:
Organizing training data (e.g., tabular, text, speech, images, and videos) on Google Cloud (e.g., Cloud Storage and BigQuery)
Gemini Enterprise Agent Platform serverless training overview
Introduction to ML in BigQuery
Ingesting structured and unstructured data from various sources into training pipelines
Introduction to Gemini Enterprise Agent Platform Pipelines
Model training using different software development kits (SDKs) (e.g., Agent Platform custom training, Kubeflow on Google Kubernetes Engine [GKE], Agent Platform AutoML, and Tabular Workflows) and organizing training on Google Cloud
Gemini Enterprise Agent Platform serverless training overview
Tabular Workflows on Agent Platform
Troubleshooting ML model training failures
Gemini Enterprise Agent Platform serverless training overview
Hyperparameter tuning
Overview of hyperparameter tuning
Create a hyperparameter tuning job
Fine-tuning foundational models from Agent Platform and Model Garden and when tuning should be considered
About supervised fine-tuning for Gemini models
3.3 Choosing appropriate hardware for training. Considerations include:
Evaluation of compute and accelerator options (e.g., CPU, GPU, and TPU)
Configure compute resources for Gemini Enterprise Agent Platform serverless training
Gemini Enterprise Agent Platform serverless training overview
Understanding the options for distributed training on GPUs and TPUs using data and model parallelism strategies
Configure compute resources for Gemini Enterprise Agent Platform serverless training
Section 4: Serving and scaling models (~20% of the exam)
4.1 Serving models. Considerations include:
Deploying models for batch and online inference using appropriate services (e.g., Agent Platform, Model Garden, Cloud Run, and GKE)
Overview of getting inferences on Agent Platform
Get batch inferences from a custom trained model
Packaging and serving models from different frameworks (e.g., PyTorch and XGBoost) using prebuilt and custom containers
Prebuilt containers for inference and explanation
Use a custom container for inference
Organizing and versioning models in Gemini Enterprise Agent Platform Model Registry
Introduction to Model Registry
BigQuery ML and Model Registry
Implementing model rollout strategies (e.g., A/B testing and canary deployments) to compare model versions
Deploy a model by using the Google Cloud console
Developing solutions for inference preprocessing and postprocessing
Use a custom container for inference
4.2 Scaling online model serving. Considerations include:
Managing and serving features using Agent Platform Feature Store
About Feature Store on Gemini Enterprise Agent Platform
Introduction to feature management in Gemini Enterprise Agent Platform
Deploying models to public and private endpoints
Deploy a model by using the Google Cloud console
Choosing appropriate hardware (e.g., CPU, GPU, TPU, and edge)
Configure compute resources for Gemini Enterprise Agent Platform serverless training
Prebuilt containers for inference and explanation
Scaling the serving backend based on the throughput (e.g., Gemini Enterprise Agent Platform Inference and containerized serving)
Overview of getting inferences on Agent Platform
Tuning ML models for training and serving in production
Overview of hyperparameter tuning
Section 5: Automating and orchestrating ML pipelines (~18% of the exam)
5.1 Developing end-to-end ML pipelines. Considerations include:
Validating data and models
Introduction to Gemini Enterprise Agent Platform Pipelines
Introduction to Gemini Enterprise Agent Platform ML Metadata
Building and orchestrating pipelines using managed or unmanaged services and from templates or custom solutions (e.g., Agent Platform Pipelines, Managed Service for Apache Airflow, and Ray on Gemini Enterprise Agent Platform)
Introduction to Gemini Enterprise Agent Platform Pipelines
Managed Service for Apache Airflow
Ray on Agent Platform overview
Ensuring consistent data preprocessing between training and serving
Perform feature engineering with the TRANSFORM clause
Introduction to Model Monitoring
5.2 Automating model retraining. Considerations include:
Determining an appropriate retraining policy
Introduction to Model Monitoring
MLOps on Gemini Enterprise Agent Platform
Deploying models in continuous integration, continuous delivery, and continuous training (CI/CD/CT) pipelines (e.g., Cloud Build)
MLOps on Gemini Enterprise Agent Platform
Introduction to Gemini Enterprise Agent Platform Pipelines
Section 6: Monitoring AI solutions (~13% of the exam)
6.1 Identifying risks to AI solutions. Considerations include:
Building secure AI systems by protecting against unintentional exploitation and leaks of data or models (e.g., data exfiltration, malicious prompting, and sharing sensitive data with LLMs) using the appropriate security tool (e.g., Regex, safety filters, and Model Armor)
Integrate Model Armor with Gemini Enterprise Agent Platform
Sensitive Data Protection overview
Aligning with responsible AI practices (e.g., monitoring for bias)
Gemini image generation and responsible AI
Overview of models on Agent Platform
Model explainability on Agent Platform (e.g., Agent Platform Inference)
Introduction to Vertex Explainable AI
Introduction to Model Monitoring
Monitor feature attribution skew and drift
6.2 Monitoring, testing, and troubleshooting AI solutions. Considerations include:
Configuring and using Model Monitoring on Gemini Enterprise Agent Platform to establish continuous evaluation metrics for production models
Introduction to Model Monitoring
Gemini Enterprise Agent Platform Model Monitoring for batch inferences
Monitoring for common issues (e.g., training-serving skew, data drift, concept drift, and feature attribution drift)
Introduction to Model Monitoring
Monitor feature attribution skew and drift
Monitoring, testing, and evaluating gen AI solutions
Gen AI evaluation service overview
Machine Learning Engineer – Final Thoughts
This PMLE study guide walked through all six exam domains, from low-code AI solutions and data collaboration to scaling, serving, pipeline automation, and monitoring. Working through each objective and its official documentation will build the practical, hands-on knowledge the exam expects.
You can also explore more GCP certification study guides on the GCP category page to keep building your skills. Have a question or tip? Leave a comment below.
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