AI in Production with GenAI and Agentic AI at Scale is a comprehensive professional guide designed to help you master the deployment of Generative AI and Agentic AI solutions in real-world environments. This advanced training program takes you from foundational concepts to fully operational, production-ready AI systems—empowering you to automate workflows, enhance decision-making, and build scalable AI-driven applications.
Course Description
Part of the AI Mastery Series, this program is developed by seasoned AI engineers and industry practitioners.
The ability to deploy AI efficiently, responsibly, and at scale has become essential for AI engineers, data scientists, machine learning practitioners, and enterprise architects. AI in Production with GenAI and Agentic AI at Scale guides you through the entire end-to-end lifecycle—from data preprocessing and model fine-tuning to deploying and scaling AI pipelines using MLOps and cloud-native tools.
Through practical labs and real-world case studies, you’ll learn how to build, deploy, and manage agentic AI systems that deliver measurable business value.
Whether you’re creating intelligent chatbots, autonomous AI agents, or enterprise-grade generative applications, this course provides the frameworks, techniques, and production methodologies needed to succeed in modern AI environments. You’ll also explore the platforms and architectures that make large-scale GenAI systems possible.
What You’ll Learn
Deploy and scale Generative AI models using modern MLOps workflows.
Design agentic AI architectures that autonomously interact with real-world systems.
Integrate GenAI solutions with enterprise data pipelines, APIs, and business platforms.
Implement monitoring, optimization, governance, and responsible AI practices.
Use cloud-native tools (AWS, Azure, GCP) to manage AI workloads at scale.
Automate end-to-end AI workflows for production-grade reliability and uptime.
Requirements
Basic knowledge of Python and machine learning concepts.
Familiarity with frameworks such as PyTorch or TensorFlow.
Understanding of cloud environments (helpful, but not required).