Applied AI/ML on Cloud Platforms: Dev, Deployment & GenAI

Description

Course Overview
Objective: This program equips students with practical and theoretical knowledge to manage
the complete AI lifecycle—from model training to deployment, monitoring, and continual
optimization. Emphasis is placed on both classical MLOps and emerging LLMOps workflows to
prepare learners for production-grade AI systems at scale.
Preparation for Job Market & Entrepreneurship: This program prepares students for roles such
as:
• MLOps Engineer
• LLMOps Engineer
• AI Operations Manager
• DataOps Engineer
• AI Infrastructure Engineer
• AI Automation Specialist

Graduates will:
1. Build production-grade ML pipelines using TensorFlow Extended (TFX), MLFlow, and
Kubeflow

2. Deploy and manage LLMs with LangChain, Triton Inference Server, and Hugging Face
Transformers
3. Set up scalable CI/CD for ML using Jenkins, ArgoCD, and GitHub Actions
4. Implement GPU/TPU-aware orchestration and edge deployment with Nvidia Triton &
ONNX
5. Monitor drift, bias, latency, and performance using Prometheus, Evidently, Grafana, and
BentoML
6. Practice security, reproducibility, and compliance with DVC, ML metadata tracking, and
lineage
7. Master real-time and batch workflows with Apache Airflow and Prefect

8. Design ethical, explainable, and cost-optimized LLMOps strategies including RAG, fine-
tuning, and prompt chaining

Learning Modality
• In-person Live Instructor-led: $11,499
• Virtual Live Instructor-led: $10,499
• Exclusive 1-on-1 Virtual Mentorship: $12,999

Admissions Requirements
1. High school diploma or GED
2. Minimum age: 18 (or parental consent if underage)