Learners will analyze Generative AI deployment environments, evaluate platform and vendor options, and apply best practices to integrate, deploy, and manage GenAI systems at scale. By the end of this course, learners will be able to design deployment architectures, assess operational trade-offs, and implement responsible GenAI solutions across real-world use cases.
Apply Generative AI Integration and Deployment Strategies
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Design scalable and responsible Generative AI deployment architectures.
Evaluate GenAI platforms, vendors, and operational trade-offs.
Apply best practices to deploy, manage, and monitor GenAI systems in production.
Skills you'll gain
Details to know
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Recently updated!
January 2026
Assessments
7 assignments
Taught in English
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There are 2 modules in this course
This module introduces the foundational concepts of integrating and deploying Generative AI solutions in real-world environments. Learners explore the GenAI development landscape, evaluate key architectural and operational considerations, and analyze real-world case studies to understand best practices for scalable, responsible, and effective GenAI deployment.
What's included
6 videos4 assignments
This module focuses on practical deployment platforms, tools, and frameworks used in Generative AI systems. Learners examine leading GenAI providers and inference frameworks, apply deployment best practices through hands-on labs and projects, and assess real-world deployment outcomes to build end-to-end GenAI deployment competence.
What's included
5 videos3 assignments
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