Google Gemini Model Selection is a comprehensive guide to understanding, evaluating, and deploying Google’s advanced Gemini AI models. Designed for developers, data scientists, and AI practitioners, this course helps you choose the right model for a variety of business and technical scenarios—and integrate Gemini capabilities into production-ready solutions with confidence.
 
Course Description
This course provides an in-depth exploration of the architecture, capabilities, and performance characteristics of the Gemini model family. You’ll learn how to compare model sizes, interpret performance trade-offs, and select the ideal model for tasks involving text, images, multi-modal inputs, and code generation.
 
Through interactive lessons and hands-on exercises, you’ll experiment with model parameters, analyze behavioral differences, and benchmark Gemini models using real-world datasets. You’ll also discover how to integrate Gemini with Google Cloud AI APIs, enabling scalable deployment and seamless incorporation into enterprise systems.
Whether you’re working on NLP, computer vision, automation, or multi-modal AI applications, this course equips you with the practical skills needed to select, optimize, and operationalize Gemini models effectively.
 
What You’ll Learn
Understanding Gemini model architecture and evolution
Choosing the right Gemini model for specific tasks and constraints
Implementing Gemini via Google Cloud AI tools and APIs
Optimizing model performance, latency, and cost-efficiency
Applying Gemini to NLP, computer vision, and code generation workflows
Benchmarking Gemini models using real-world datasets and evaluation metrics
 
Requirements
Basic familiarity with Python and foundational AI concepts
Some experience with Google Cloud Platform (GCP) services
Access to Pluralsight to view full course modules