Automate & Secure LLM Deployments

This course is part of Build Next-Gen LLM Apps with LangChain & LangGraph Specialization

Instructors: Starweaver

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Design automated CI/CD pipelines for LLM deployments using containerization and infrastructure as code.
Apply security best practices including API protection, prompt injection prevention, and compliance frameworks.
Configure production monitoring, auto-scaling, and cost optimization for enterprise LLM systems.

Skills you'll gain

Tools you'll learn

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This course is part of the Build Next-Gen LLM Apps with LangChain & LangGraph Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

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.

Build automated CI/CD pipelines for LLM applications using GitHub Actions, Docker containerization, and blue-green deployment strategies. You'll configure workflows that automatically build container images, scan them for vulnerabilities, and deploy them with zero downtime. Through hands-on practice, you'll transform manual deployment processes into reliable automation that builds, scans, pushes, and blue-green deploys LLM applications to production environments.

What's included

4 videos2 readings1 peer review

4 videos Total 35 minutes

Welcome to Automated LLM Deployments 4 minutes
Introduction to LLM CI/CD Pipelines 10 minutes
Containerizing LLM Applications with Docker 8 minutes
Infrastructure as Code for LLM Deployments 14 minutes

2 readings Total 10 minutes

Welcome to the Course: Course Overview 5 minutes
MLOps Best Practices Guide 5 minutes

1 peer review Total 20 minutes

Hands-On-Learning: Automate LLM Pipeline: Deploy TechCorp's Multi-Model Infrastructure 20 minutes

Implement comprehensive security controls for production LLM APIs using identity and access management, secret management, and automated security scanning. You'll configure least-privilege IAM roles, set up secure secret ARNs for API keys and credentials, and run Trivy scans to confirm zero critical security findings. The module covers LLM-specific security concerns including prompt injection prevention, rate limiting, and audit logging for compliance requirements

What's included

3 videos1 reading1 peer review

Ensure production reliability and cost efficiency by implementing comprehensive monitoring, automated rollback strategies, and cost optimization techniques for LLM systems. You'll create CloudWatch alarms to track critical metrics, simulate latency spikes to verify automated rollback rules, and configure auto-scaling policies that handle traffic variations. The module also covers cost optimization strategies including response caching, intelligent model routing, and resource management to reduce operational expenses

What's included

4 videos1 reading1 assignment2 peer reviews

4 videos Total 42 minutes

Monitoring LLM Performance and Costs 10 minutes
Auto-scaling Strategies for LLM Services 14 minutes
Cost Optimization Techniques 15 minutes
Next Steps in LLM Operations 4 minutes

1 reading Total 5 minutes

Production LLM Monitoring Guide 5 minutes

1 assignment Total 20 minutes

Automate & Secure LLM Deployments 20 minutes

2 peer reviews Total 80 minutes

Hands-On-Learning: Scale to Million Users: Optimize GlobalAI's Production System 20 minutes
Project: Deploy Production LLM Platform: TechUnicorn's Enterprise Solution 60 minutes

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Instructors

Starweaver

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554 Courses 1,032,012 learners

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