Automate & Secure LLM Deployments
Every day, companies waste thousands of dollars on poorly deployed LLM applications—experiencing downtime, security breaches, and runaway costs that could have been prevented. This comprehensive course teaches you to build automated CI/CD pipelines specifically designed for LLM applications, implement enterprise-grade security controls, and optimize for scale and cost. Through hands-on labs based on real-world scenarios, you'll work with Docker, Kubernetes, Terraform, and cloud platforms to build production-ready systems. Each module includes practical exercises where you'll solve actual deployment challenges faced by companies scaling LLM applications.
For DevOps, platform, and AI engineers deploying and operating large-scale LLM systems, with a focus on automation, security, cost optimization, and building reliable, high-performance AI platforms.
Learners should have a basic understanding of Docker, APIs, and cloud platforms. Familiarity with CI/CD, Python, and basic security practices is helpful but not required.
By course completion, you'll have deployed a secure, scalable LLM platform capable of handling millions of requests while maintaining 99.9% uptime. Perfect for DevOps engineers, platform engineers, and technical professionals ready to operationalize LLM applications.
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Related AI Lessons
⚡
⚡
⚡
⚡
Safeguard AI — a multilingual disaster preparedness assistant powered by Gemma 4
Dev.to · Kamishetti Varshitha
"Gemma 4 Deep Dive: Multi-Token Prediction and the New Frontier of Edge AI"
Dev.to · M. Khubaib Zafar
RAG vs Fine-Tuning
Dev.to · Khishamuddin Syed
Structured Output Validation: Pydantic/Zod vs In-Prompt Schema vs JSON Mode
Dev.to · Gabriel Anhaia
🎓
Tutor Explanation
DeepCamp AI