Course Overview
Vertex AI Model Garden provides enterprise-ready foundation models, task-specific models, and APIs. Model Garden can serve as the starting point for model discovery for various different use cases. You can kick off a variety of workflows including using models directly, tuning models in Generative AI Studio, or deploying models to a data science notebook.
In this class, after being introduced to Vertex AI as a machine learning platform through the lens of Model Garden. You will learn how to leverage re-trained models as part of your machine learning workflow and how to fine-tune models for your specific applications.
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various different use cases.
Prerequisites
To get the most out of this course, participants should have:
- Prior completion of Machine Learning on Google Cloud (MLGC) course or the equivalent knowledge of TensorFlow/Keras and machine learning.
- Experience scripting in Python and working in Jupyter notebooks to create machine learning models.
Course Objectives
- Understanding the model options available within Vertex AI Model Garden
- Incorporate models in Vertex AI Model Garden in your machine learning workflows
- Leverage foundation models for generative AI use cases
- Fine-tune models to meet your specific needs
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