The Cloud Playbook: Step-by-Step Guide To Learn LLMOps

What Makes This Guide Complete?

1. Detailed Project Tasks: Each project now includes specific, actionable tasks you can follow step-by-step, from installation commands to code examples.

2. AWS-Focused: All cloud examples use AWS services (EKS, SageMaker, Bedrock, Lambda, S3, CloudWatch) to maintain consistency and depth.

3. Progressive Complexity: Projects build on each other, from local deployment to enterprise multi-tenant platforms.

4. Clear Success Metrics: Each phase has measurable outcomes so you know when you're ready to move forward.

5. Real-World Focus: Projects simulate actual enterprise scenarios you'll encounter in LLMOps roles.

Who Is This Guide For?

Perfect For:

• Platform Engineers managing Kubernetes, cloud infrastructure, and developer tooling who want to specialize in AI/ML operations

• DevOps Engineers with 2+ years of experience in CI/CD, monitoring, and infrastructure automation, looking to enter the rapidly growing LLMOps field
• Site Reliability Engineers (SRE) who understand distributed systems and want to apply their skills to AI model deployment and scaling
• Cloud Engineers working with AWS who need to add AI/ML capabilities to their skill set for career advancement
• Infrastructure Engineers familiar with containerization and orchestration who want to transition into the high-demand LLMOps market

Prerequisites:

• Technical Foundation: Comfortable with Linux, Docker, and basic cloud services (AWS preferred)
• Programming Skills: Basic Python knowledge (you'll learn ML-specific libraries)
• Infrastructure Experience: Understanding of load balancers, databases, and networking concepts
• DevOps Mindset: Experience with automation, monitoring, and infrastructure-as-code principles