PluralSight – GCP-PMLE Scaling Prototypes into ML Models 2026
English | Tutorial | Size: 154.54 MB
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.
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.
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