LLMOps And AIOps Bootcamp With 8 End To End Projects

LLMOps and AIOps Bootcamp with 8 End-to-End Projects is a hands-on, production-focused program designed to help engineers deploy, monitor, scale, and operate Large Language Models (LLMs) and AI systems in real-world environments.

This course goes far beyond experimentation. It focuses on real production workflows, combining DevOps, MLOps, LLMOps, and AIOps practices to build enterprise-ready AI systems using modern tooling such as Jenkins, Docker, Kubernetes, AWS/GCP, Prometheus, and Vector Databases.

By the end of the bootcamp, learners gain practical experience building fully automated CI/CD pipelines, scalable LLM deployments, observability stacks, and AI-driven monitoring systems—all through 8 complete end-to-end projects.

Course Snapshot

• Course Type: Bootcamp (Hands-on, Project-Based)

• Skill Level: Intermediate → Advanced

• Language: English (Spanish auto-captions available)

• Last Updated: December 2025

• Access: Included in Udemy subscription plans

• Learning Style: Real-world projects + production architecture

• Platforms: Web, Mobile, TV

• Certification: Included

What This Course Is About

This bootcamp bridges the gap between AI model development and real-world production deployment.

You’ll learn how to:

• Deploy LLM-based systems at scale

• Automate AI pipelines with CI/CD

• Monitor AI applications using observability tools

• Manage AI infrastructure on cloud platforms

• Operate vector databases for RAG and GenAI systems

• Apply AIOps concepts for proactive monitoring and automation

Rather than isolated demos, the course emphasizes full lifecycle AI operations, from source code to production monitoring.

What You’ll Learn

LLMOps Foundations

• What LLMOps is and how it differs from traditional MLOps

• Lifecycle management of LLM-based applications

• Best practices for production GenAI systems

CI/CD for AI Systems

• Build CI/CD pipelines using Jenkins

• Automate testing, container builds, and deployments

• Version control and rollout strategies for AI services

Containerization & Orchestration

• Dockerizing AI and LLM applications

• Deploying scalable services using Kubernetes (K8s)

• Managing microservices for AI workloads

Cloud Deployment (AWS & GCP)

• Deploy AI systems on AWS and Google Cloud Platform

• Infrastructure patterns for scalable GenAI workloads

• Cost-aware and secure cloud architecture

Vector Databases & RAG Systems

• Use vector databases for semantic search and retrieval

• Power production-ready RAG pipelines

• Integrate vector stores into LLM workflows

Monitoring & Observability (AIOps)

• Monitor AI systems with Prometheus

• Track latency, cost, drift, and failures

• Apply AIOps concepts for automated alerts and insights

End-to-End Projects (Key Highlight)

This bootcamp includes 8 full production-grade projects, covering:

• CI/CD pipelines for AI applications

• Cloud-based LLM deployments

• Kubernetes-managed AI services

• Vector database–powered RAG systems

• Monitoring dashboards and alerting pipelines

• AI infrastructure automation using DevOps best practices

Each project simulates real enterprise scenarios, ensuring learners can apply the same patterns in professional environments.

Tools & Technologies Covered

• Jenkins (CI/CD)

• Docker

• Kubernetes (K8s)

• AWS & Google Cloud Platform

• Prometheus (Monitoring & Observability)

• Vector Databases

• LLM deployment frameworks

Who This Course Is For

This bootcamp is ideal for:

• AI Engineers

• MLOps & LLMOps Engineers

• DevOps Engineers transitioning into AI

• Data Scientists deploying models to production

• Software Engineers working on GenAI systems

It’s especially valuable for professionals aiming to move from notebooks to production-grade AI systems.

Practical Value

After completing this course, you’ll be able to:

• Deploy LLM applications at production scale

• Build automated CI/CD pipelines for AI workloads

• Operate and monitor AI systems reliably

• Implement observability for GenAI and RAG systems

• Apply AIOps techniques to reduce downtime and failures

Industry & Career Relevance

LLMOps and AIOps skills are rapidly becoming must-have competencies as organizations move GenAI from prototypes into mission-critical systems.

This bootcamp prepares learners for roles such as:

• LLMOps Engineer

• AI Platform Engineer

• MLOps Engineer

• AI Infrastructure Engineer

Final Verdict

LLMOps and AIOps Bootcamp with 8 End-to-End Projects is a strong, production-oriented course for professionals serious about deploying and operating AI systems at scale.

If your goal is to move beyond experimentation and build robust, monitored, cloud-native LLM applications, this bootcamp delivers the right mix of tools, architecture, and hands-on experience.