Design, deploy, and operate AI systems on AWS, GCP, Azure, and Vercel with MLOps and agentic architectures
Most courses on Generative AI teach you to build demos. This course teaches you to take AI applications to production. You'll learn to design, deploy, and operate modern AI systems using RAG, Agents, MCP, MLOps and cloud architectures on AWS, Azure, GCP and Vercel, with a particularly deep focus on AWS.
Over four weeks you'll build four real projects that evolve from a SaaS application with authentication and professional deployment to enterprise-level multiagent systems. You'll work with Next.js, Vercel, AWS App Runner, Bedrock, Lambda, API Gateway, SageMaker, Terraform, GitHub Actions, LangFuse, Aurora Serverless, SQS and MCP, learning how to integrate all these technologies into production-ready architectures.
You won't just learn how to use language models. You'll discover how to select the right architecture for each use case, implement RAG efficiently, connect agents through MCP, deploy proprietary and open source models, automate infrastructure with IaC, build CI/CD pipelines and monitor performance, costs, security and observability in real AI applications.
By the end of the course, you'll have developed the mindset and skills of a Production AI Engineer capable of designing scalable, resilient, and secure systems for companies and startups. If you want to go from building prototypes to creating AI products that can serve thousands of users, this is the course that shows you exactly how to do it.
You'll build generative AI SaaS applications using Next.js, Vercel, AWS, and Clerk, ready for real users from day one.
You'll build scalable architectures on AWS with Lambda, API Gateway, S3, CloudFront, Route 53, App Runner and other managed services.
You'll integrate models like GPT-5, Claude, Nova and open source models through Amazon Bedrock and deploy inference with SageMaker.
You'll develop RAG-based solutions, MCP, and agentic architectures capable of accessing tools, knowledge, and enterprise data.
You'll manage Infrastructure as Code with Terraform and create CI/CD pipelines through GitHub Actions to deploy with a single click.
You'll implement Agentic AI applications using Aurora Serverless, Lambda, SQS, and Bedrock AgentCore to coordinate multiple intelligent agents.
You'll learn how to incorporate observability with LangFuse, monitor costs, apply guardrails, and design safe and resilient solutions.
You'll deploy applications and agents on AWS, Azure, GCP, and Vercel, understanding when to use each platform based on your use case.
You'll finish the course having built four complete, production-ready AI applications, applying the same practices used by tech companies.
Ligency Team is an international team of experts in programming, artificial intelligence, data science and technology, founded by Kirill Eremenko and Hadelin de Pontevés, creators of some of the world's most popular courses in these disciplines. With millions of students and a multidisciplinary team of over 20 professionals, our mission is to offer practical, high-quality training that helps people at any level develop relevant technological skills. Since 2018, our courses have been available in Spanish thanks to the collaboration with Juan Gabriel Gomila and Frogames Formación, bringing the best educational content to the Spanish-speaking community.
Mathematician, Certified Unity Instructor, and Online Instructor who has trained over 600,000 students worldwide across different platforms such as Udemy and Platzi. CEO of Frogames Formación and driving force behind this platform, bringing all his knowledge in Mathematics, Machine Learning, Videogames, AI and Blockchain among others.
Buy the course once and access it for life
Access all Frogames courses, communities and extras
Access all courses in the Path of
