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Professional Machine Learning Engineer
Topics
TensorFlow

Professional Machine Learning Engineer

Build, deploy, and monitor production ML and generative AI solutions on Gemini Enterprise Agent Platform, and track your readiness for the Professional Machine Learning Engineer exam.

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Concept

TensorFlow

Topic in 4 exam domains

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TensorFlow · Concept
TensorFlow · Service

Also called: TensorFlow model

A machine learning framework supported for custom training on Gemini Enterprise Agent Platform.

Where this appears on the exam

4 subdomains in 3 of 6 domains

Domain 1
Architecting low-code AI solutions
~13% of exam
1. 1.1Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform
  - Generating predictions using BigQuery ML
Domain 2
Collaborating within and across teams to manage data and models
~16% of exam
1. 2.2Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and Colab Enterprise)
2. 2.3Tracking and running ML experiments
  - 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
Domain 3
Scaling prototypes into ML models
~21% of exam
1. 3.3Choosing appropriate hardware for training
  - Understanding the options for distributed training on GPUs and TPUs using data and model parallelism strategies

Related topics

FoundationsThis topicWorks alongsideManaged Service for Apache SparkService

TensorFlowConcept

TPU v4Concept Vertex AI TrainingService

Points to what builds on itWorks with or alternative toHover or tap a line to see what connects the two

TensorFlow is part of Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark provides a pre-installed environment for machine learning and data science with popular libraries such as [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), and [XGBoost](https://www.nvidia.com/en-us/glossary/xgboost/).”
TensorFlow works with TPU v4 From the official documentation: “If you're using TPU v4 and TensorFlow 2.10.1 or newer, follow the instructions for [TPU v2 and v3](#tf-v2-v3).”
TensorFlow works with Vertex AI Training From the official documentation: “Create a Gemini Enterprise Agent Platform custom job for training a TensorFlow model.”

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Last updated November 2, 2026

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