Empowering GenAI on AWS EKS: Deploy, Experiment, and Scale Advanced AI Workflows

Unlock the power of AI with hands-on expertise in Kubernetes, Amazon EKS, Terraform, vLLM, JARK Satck, RayServe & BioNeMo.

Why This Course?

The ability to build, deploy, and scale AI applications efficiently is no longer a luxury—it’s a necessity.

AI engineers, DevOps professionals, and software developers who can bridge the gap between ML and scalable infrastructure are in high demand.

This course will equip you with end-to-end, production-grade AI deployment skills using Amazon EKS, Kubernetes, Terraform, JARK Stack (Jypyter, Argo Workflows, Ray, Kubernetes), GPU, vLLM, Grafana, Prometheus and BioNeMo, and more—the same technologies used by leading AI-driven organizations.

You'll learn not just theory, but practical, real-world workflows for LLM deployment, distributed training, GPU optimization, and MLOps automation—ensuring you're ready to build and scale AI solutions at any organization.

🎯 Course Milestones:

You'll begin by mastering the fundamentals of Generative AI and ML workflows, and understanding how Kubernetes enables large-scale model deployment, focusing on Amazon EKS for enterprise-ready solutions.

Next, you’ll provision GPU-enabled EKS clusters, configure auto-scaling using Karpenter and Terraform, and optimize networking and IAM roles, ensuring a robust and automated infrastructure for AI workloads.

With the foundation set, you'll dive into the JARK stack (JupyterHub, Argo Workflows, Ray, and Kubernetes) for seamless model experimentation, workflow automation, and scaling distributed workloads.

From experimentation, you’ll transition into scalable inference, leveraging RayServe and vLLM to deploy high-performance LLMs, optimize GPU memory, and integrate Open WebUI for real-time interactions.

To complete the ML and GenAI pipeline, you'll tackle distributed model training with BioNeMo, handling large datasets, multi-GPU orchestration, and performance tuning for efficient large-scale learning.

By the final stage, you’ll be ready for your graduation project—building a real-world GenAI-powered chat application, integrating the entire ML workflow from training to inference while implementing CI/CD and monitoring strategies.

Through hands-on labs and project-based learning, you'll gain the practical expertise to deploy enterprise AI solutions, advance your career, and step into leadership roles in cloud-native AI engineering.

🌻What You’ll Gain

This course transforms your career by giving you the hands-on experience and expertise needed to operate at the intersection of AI, ML, DevOps, and cloud-native architectures.

🚀 By the end of this course, you will:

✅ Deploy AI Models at Scale – Learn Kubernetes and Amazon EKS to run AI workloads on GPU-powered clusters with high availability and efficiency.

✅ Master AI Inference with RayServe – Scale your LLM deployments seamlessly with RayServe and vLLM, ensuring fast and cost-effective inferencing.

✅ Optimize AI Workloads – Use BioNeMo and distributed training techniques to maximize GPU utilization and optimize ML model training at scale.

✅ Hands-on Real-World Projects – Work on a full-fledged GenAI chat application, gaining practical expertise that applies directly to industry use cases.

✅ Career Acceleration – Position yourself as an AI/ML platform engineer, MLOps expert, or cloud-native AI specialist, opening doors to high-paying roles in top-tier companies.

🎓 Why Learn from This Course?

Most AI courses focus on model training but neglect the critical aspects of deploying, scaling, and operationalizing AI systems. This course bridges the gap.

You won’t just learn how to train AI models—you’ll learn how to deploy, optimize, and maintain them at scale in real-world environments.

✔️ Designed for professionals who want to apply DevOps & Kubernetes to ML

✔️ Project-based learning ensures practical expertise

✔️ Covers the entire AI lifecycle from training to production deployment

✔️ Instructor-led deep dives on critical AI deployment patterns

This is the only course that provides a complete skill set in AI, ML, DevOps, and cloud-native architectures—ensuring you stay ahead of the curve in one of the most sought-after fields in tech today.