GenAI Model Development and Production Engineering

About this course

Frustrated with AI models that can't understand your specific domain or scale beyond demo environments? Most organizations struggle to transform promising AI prototypes into robust, production-ready systems that deliver consistent value under real-world enterprise demands, leaving breakthrough potential unrealized. This comprehensive GenAI Model Development and Production Engineering course transforms you into a complete GenAI specialist who can fine-tune foundation models for specialized domains, architect resilient deployment infrastructure, and maintain GenAI models in production that scale reliably to millions of users. You'll gain a deep understanding of the GenAI development process, mastering advanced fine-tuning techniques including parameter-efficient methods such as LoRA, implementing enterprise-grade deployment strategies with comprehensive monitoring and automated maintenance, and building production systems using advanced optimization techniques such as semantic caching, hybrid routing, and edge deployment. This course is designed for professionals engineering AI systems at scale, including ML engineers building production-ready GenAI models, DevOps engineers managing GenAI production engineering workflows, platform engineers developing scalable AI infrastructure, and technical architects designing end-to-end enterprise AI solutions. Whether you're optimizing model performance, deploying large language models, or ensuring GenAI in production operates reliably across cloud environments, this course equips you with practical skills to deliver secure, scalable, and high-performance AI systems. Participants should have completed foundational courses in generative AI, data engineering, and AI agent development. Proficiency in advanced Python programming and experience with machine learning frameworks are essential. Learners should also have hands-on familiarity with cloud platforms, Docker, Kubernetes, and the model development process, including model training, evaluation, deployment, and production system architecture. Prior experience with GenAI model development or MLOps concepts will help learners maximize the value of this course. By the end of this course, learners will be able to execute advanced GenAI model development workflows, including LoRA-based fine-tuning and domain-specific model adaptation. They will implement enterprise-grade GenAI production engineering strategies with automated deployment, monitoring, container orchestration, and scalable infrastructure. Additionally, learners will build robust production monitoring systems with real-time alerting and apply advanced optimization techniques including semantic caching, hybrid routing, and edge deployment to deliver reliable, resilient, and production-ready generative AI systems.

Course objectives

• master fine-tuning techniques for generative AI models
• develop production monitoring systems with real-time alerting
• create workflows that ensure consistent performance in AI models