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(GCP-PMLE) Scaling Prototypes into ML Models

Gain the expertise to transition machine learning prototypes into scalable, production-ready models using Google Cloud. This course will teach you how to effectively scale ML prototypes into high-performing production models.

Intermediate
58m

Created by Victor Dantas

Last Updated Apr 13, 2026

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  • Course

(GCP-PMLE) Scaling Prototypes into ML Models

Gain the expertise to transition machine learning prototypes into scalable, production-ready models using Google Cloud. This course will teach you how to effectively scale ML prototypes into high-performing production models.

Intermediate
58m

Created by Victor Dantas

Last Updated Apr 13, 2026

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What you'll learn

Are you aiming to pass the Google Cloud Professional Machine Learning Engineer exam or hone your ability to scale AI solutions? In this course, (GCP-PMLE) Scaling Prototypes into ML Models, you'll gain the ability to scale ML prototypes into high-performing production models. First, you'll explore how to select the right ML frameworks and design models for interpretability. Next, you'll discover how to orchestrate training using Vertex AI, distributed pipelines, and hyperparameter tuning. Finally, you'll learn how to choose optimal hardware accelerators like GPUs and TPUs for your specific workloads. When you're finished with this course, you'll have the skills and knowledge of Google Cloud machine learning needed to scale ML prototypes into high-performing production models.

(GCP-PMLE) Scaling Prototypes into ML Models
Intermediate
58m
Table of contents

About the author
Victor Dantas - Pluralsight course - (GCP-PMLE) Scaling Prototypes into ML Models
Victor Dantas
16 courses 0.0 author rating 0 ratings

IT professional with a PhD in Software-Defined Networking and certified Cloud Architect on Microsoft Azure and Google Cloud Platform (GCP). With a background ranging from software development and systems engineering to professional services for Cloud migration, I have accumulated experience in Hybrid cloud infrastructure, cloud architecture and design, Infrastructure-as-Code (IaC) as well as Agile/DevOps practices. My current interests involve AI / Machine Learning and Internet-of-Things. (IoT).

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