GenAI Model Development and Production Engineering from Coursera

This comprehensive production engineering course transforms you into a complete GenAI specialist who can fine-tune foundation models for specialized domains, architect bulletproof deployment infrastructure, and maintain AI systems that scale reliably to millions of users. You'll master advanced fine-tuning techniques including parameter-efficient methods like LoRA, implement enterprise-grade deployment strategies with comprehensive monitoring and automated maintenance, and build production systems with advanced optimization techniques including semantic caching, hybrid routing, and edge deployment strategies.

This course is designed for professionals engineering AI systems at scale, including ML engineers focused on production-ready models, DevOps engineers managing AI deployments, platform engineers building robust infrastructure, and technical architects designing end-to-end scalable AI solutions. Whether you're optimizing model throughput or managing cross-platform reliability, this course supports your role in delivering high-performance GenAI systems in enterprise environments.

Participants should have completed foundational courses in generative AI, data engineering, and AI agent development. Proficiency in advanced Python programming and experience with ML frameworks are essential. Learners are expected to have hands-on familiarity with cloud platforms, containerization technologies like Docker and Kubernetes, and a solid understanding of model training, evaluation, and production system architecture.

By the end of this course, learners will be able to execute advanced fine-tuning workflows including LoRA and domain-specific model adaptations. They will implement enterprise-grade deployment strategies with automation, monitoring, and container orchestration. Additionally, learners will construct robust production monitoring systems with real-time alerting and apply advanced optimization methods such as caching, hybrid routing, and edge deployment for scalable, resilient AI system performance.

What's inside

Syllabus

GenAI Foundations

In this module, you’ll learn how to design and build robust GenAI applications by exploring the core architecture and components of modern AI systems. You’ll set up a professional development environment—configuring SDKs, tooling, and data pipelines—and examine real-world enterprise implementations to see how organizations leverage GenAI for competitive advantage. Through expert-led walkthroughs, hands-on setup exercises, and case-study analyses, you’ll gain the skills to deploy scalable, production-ready generative AI solutions.

Career center

Learners who complete GenAI Model Development and Production Engineering will develop knowledge and skills that may be useful to these careers:

Machine Learning Operations Engineer

A Machine Learning Operations Engineer is critical for bridging the gap between model development and successful deployment, ensuring AI systems run reliably and efficiently in production. This course, GenAI Model Development and Production Engineering, is an exceptional fit for this career, directly addressing core competencies. Learners master enterprise-grade deployment strategies, including automation, monitoring, and container orchestration, fundamental to MLOps. The course emphasizes architecting bulletproof infrastructure, enabling you to maintain AI systems that scale reliably to millions of users. Constructing robust production monitoring systems with real-time alerting and applying advanced optimization methods like caching and hybrid routing directly prepare you for success. This expertise in scalable, resilient GenAI system performance is indispensable for any aspiring Machine Learning Operations Engineer.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and maintains AI models, often with a specific focus on bringing them from prototype to robust production systems. This course is explicitly designed for ML engineers focusing on production-ready models, aligning perfectly with the role. You will learn to fine-tune GenAI models for specialized domains, implement enterprise-grade deployment strategies, and build production systems with advanced optimization techniques such as semantic caching and hybrid routing. The comprehensive training in model development, evaluation frameworks, and scalable infrastructure empowers a Machine Learning Engineer to deliver high-performance GenAI systems in demanding enterprise environments, ensuring models consistently deliver value.

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Artificial Intelligence Architect

An Artificial Intelligence Architect designs the overarching structure and components of scalable AI solutions, ensuring they meet enterprise demands for performance and reliability. The GenAI Model Development and Production Engineering course directly supports this role by teaching you to design and build robust GenAI applications and architect bulletproof deployment infrastructure. You will gain expertise in exploring the core architecture of modern AI systems, examining real-world enterprise implementations, and crafting strategic adoption plans for new GenAI innovations. This comprehensive understanding of end-to-end scalable AI solutions is crucial for an Artificial Intelligence Architect to lead the design and integration of advanced GenAI systems within complex organizational frameworks.

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Platform Engineer

A Platform Engineer builds and maintains the robust infrastructure upon which software applications, including advanced AI systems, operate efficiently. The course GenAI Model Development and Production Engineering directly addresses the needs of platform engineers building robust infrastructure for AI. You will learn to design robust infrastructure for enterprise-grade GenAI systems, implement real-time monitoring and alerting, and automate maintenance workflows to ensure long-term reliability. Proficiency in containerization technologies like Docker and Kubernetes, and hand-on familiarity with cloud platforms, are foundational elements emphasized. This expertise enables a Platform Engineer to create and manage the scalable, resilient environments essential for high-performance GenAI applications.

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AI Infrastructure Engineer

An AI Infrastructure Engineer specializes in architecting, building, and maintaining the foundational systems that host and run artificial intelligence models at scale. This course is highly relevant for an AI Infrastructure Engineer, providing deep insights into constructing bulletproof deployment infrastructure and maintaining AI systems that scale reliably to millions of users. Learners will master implementing enterprise-grade deployment strategies with automation and container orchestration, along with practical experience in cloud platforms. The focus on designing robust GenAI system infrastructure and applying advanced optimization methods like edge deployment ensures you can build and manage the underlying technological backbone for cutting-edge generative AI solutions.

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DevOps Engineer

A DevOps Engineer focuses on automating and streamlining the software development lifecycle, ensuring continuous integration, delivery, and deployment of applications. This course is highly beneficial for DevOps engineers managing AI deployments, offering specialized knowledge for GenAI systems. You will learn to architect bulletproof deployment infrastructure, automate maintenance workflows for long-term reliability, and implement real-time monitoring and alerting for enterprise-grade GenAI systems. Proficiency with cloud platforms and containerization technologies like Docker and Kubernetes, which are course prerequisites, aligns perfectly. This expertise helps a DevOps Engineer optimize the throughput and manage the cross-platform reliability of AI applications, driving operational excellence for generative AI.

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GenAI Solutions Engineer

A GenAI Solutions Engineer designs, develops, and implements generative AI solutions tailored to specific business needs, often bridging technical development with client requirements. This course provides a comprehensive toolkit for a GenAI Solutions Engineer, enabling you to fine-tune foundation models for specialized domains and architect production-ready systems. You will gain skills in designing and building robust GenAI applications, setting up professional development environments, and examining real-world enterprise implementations. The ability to implement enterprise-grade deployment strategies and apply advanced optimization techniques ensures you can deliver scalable, high-performance solutions that truly address customer challenges and enterprise demands. This course is key to turning GenAI potential into realized value.

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Technical Lead Machine Learning

A Technical Lead Machine Learning oversees the development and deployment of machine learning systems, guiding teams and setting technical direction. This comprehensive course prepares a Technical Lead Machine Learning with the deep, holistic understanding required for leadership. It covers GenAI foundations, model development for specialized needs, and critical production engineering aspects like deployment, monitoring, and automated maintenance. The insights into enterprise applications, future trends, and strategic adoption plans equip you to evaluate new innovations and steer your team towards scalable, resilient AI system performance. This broad expertise enables you to make informed decisions and advance your team's capabilities in enterprise AI deployment.

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Site Reliability Engineer

A Site Reliability Engineer ensures the reliability, scalability, and performance of production systems, including complex AI infrastructures. This course, GenAI Model Development and Production Engineering, strongly aligns with the responsibilities of a Site Reliability Engineer by focusing on maintaining AI systems that scale reliably to millions of users. You will learn to design robust infrastructure, implement real-time monitoring and alerting, and automate maintenance workflows for enterprise-grade GenAI systems. The emphasis on applying advanced optimization methods and constructing robust production monitoring tools directly prepares you to ensure the long-term reliability and resilient performance of critical generative AI applications, minimizing downtime and maximizing efficiency.

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Director of Artificial Intelligence Engineering

A Director of Artificial Intelligence Engineering provides strategic leadership for AI development and deployment initiatives, overseeing technical teams and roadmaps. This course offers a holistic view into GenAI model development and production engineering, critical for a Director of Artificial Intelligence Engineering. It covers foundational architectures, advanced fine-tuning techniques, enterprise-grade deployment, and robust monitoring. Furthermore, the 'Future Trends' module explores emerging technologies and strategic adoption plans, equipping you with the foresight to evaluate and integrate new GenAI innovations. This comprehensive understanding of the full GenAI lifecycle, from conceptualization to scalable production, is essential for making informed strategic decisions and driving successful AI engineering outcomes.

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Applied Scientist

An Applied Scientist bridges theoretical research with practical application, developing and deploying advanced AI solutions to real-world problems. This course, GenAI Model Development and Production Engineering, may be useful for an Applied Scientist, particularly those involved in bringing cutting-edge GenAI models to production. It covers advanced fine-tuning techniques like LoRA for specialized domains and building production systems with advanced optimization techniques such as semantic caching. While often requiring an advanced degree, the course's focus on robust deployment, monitoring, and scaling of GenAI systems provides valuable practical skills to ensure that scientific breakthroughs are transformed into reliable, high-performance applications that deliver consistent value in enterprise environments.

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Research Engineer

A Research Engineer conducts exploratory work to adapt and apply new technologies, often translating research prototypes into deployable solutions. This course may be useful for a Research Engineer, especially when the goal is to bring experimental GenAI models to a production-ready state. It provides comprehensive training in fine-tuning GenAI models for specialized business needs and implementing evaluation frameworks that ensure reliability, which is crucial for bridging research and development. The module on production engineering, covering deployment, monitoring, and maintenance of enterprise-grade GenAI systems, directly supports the practical application of research, ensuring that novel solutions are robust, scalable, and capable of performing in real-world applications. An advanced degree is often required for this role.

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Cloud Engineer

A Cloud Engineer designs, implements, and manages cloud infrastructure and services, ensuring scalability, security, and efficiency. This course may be useful for a Cloud Engineer looking to specialize in artificial intelligence and machine learning infrastructure. The course emphasizes proficiency with cloud platforms and containerization technologies like Docker and Kubernetes, which are fundamental to deploying and managing GenAI systems at scale. You will gain insights into architecting bulletproof deployment infrastructure and applying advanced optimization methods like edge deployment strategies. This knowledge enables a Cloud Engineer to build and optimize the cloud environments necessary for robust, high-performance GenAI applications, ensuring they meet enterprise-grade demands.

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Data Engineer

A Data Engineer designs, builds, and manages robust data pipelines and infrastructure, ensuring data is accessible, reliable, and performant for analytical and AI applications. While the primary focus of this course is GenAI model development and production engineering, it may be useful for a Data Engineer looking to specialize in AI-driven data solutions. The course’s prerequisites in data engineering and its coverage of setting up professional development environments, including data pipelines for GenAI applications, provide an understanding of the critical data infrastructure needed. This perspective helps in designing data systems that seamlessly feed robust, production-ready GenAI models, as well as understanding the downstream requirements for optimal model performance and maintenance in enterprise environments. This holistic view of the GenAI lifecycle enhances a Data Engineer's ability to support AI initiatives.

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Technical Product Manager

A Technical Product Manager defines product vision, strategy, and roadmaps, often for complex technical products like AI applications, requiring a deep understanding of the underlying technology. This course may be useful for a Technical Product Manager focused on GenAI products. It provides comprehensive knowledge of GenAI model development, production engineering, and future trends, enabling you to understand technical feasibility and deployment challenges. You will gain insights into enterprise implementations, advanced fine-tuning, and scalable deployment strategies. This understanding allows a Technical Product Manager to make informed decisions about product features, technical debt, and market opportunities, effectively translating complex GenAI capabilities into valuable, deployable products that meet market demands.

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