AI System Design & MLOps: From Raw Data to AWS Kubernetes

About This Course

This course teaches how to build a complete AI system from raw data to production deployment. It focuses on real-world architecture, combining data engineering, machine learning, APIs, and cloud deployment into one end-to-end workflow.

Quick Details

• Rating: 4.8 / 5
• Students: 99
• Duration: 14 Hours
• Resources: 14 Downloadable Files
• Language: English
• Certificate: Yes
• Access: Lifetime
• Price: ₹519 (Discounted)

What You’ll Learn

• Build end-to-end AI systems from data to deployment
• Design ML pipelines with SQL and feature engineering
• Track experiments using MLflow and version data with DVC
• Develop APIs using FastAPI
• Detect model drift and automate retraining workflows
• Containerize applications using Docker
• Deploy scalable systems using Amazon Web Services (ECR & EKS)
• Integrate data, ML, APIs, monitoring, and cloud into one system
• Learn production-first thinking for AI system design

LLMOps Masterclass 2026 – Generative AI – MLOps – AIOps

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Key Topics Covered

• MLOps Fundamentals
• AI System Design
• API Development
• Cloud Deployment
• Model Monitoring
• Data Engineering

Why This Course

This course focuses on practical system design rather than just building models. It helps you understand how real-world AI systems are built, deployed, and maintained in production environments.

Who Should Take This

• Machine learning engineers
• Data scientists moving to production roles
• Backend developers working with AI systems
• Anyone interested in MLOps and system design

Final Thoughts

A strong course for learning end-to-end AI system design and MLOps. It is ideal for learners who want to move beyond models and build scalable, production-ready AI solutions.