Ai System Design & Mlops: From Raw Data To Aws Kubernetes

Ai System Design & Mlops: From Raw Data To Aws Kubernetes
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

What you’ll learn

Build an end-to-end AI system from raw data to cloud deployment using real-world architecture

Design ML pipelines with SQL, feature engineering, and leakage-safe model training

Use MLflow and DVC for experiment tracking, data versioning, and reproducible pipelines

Develop production-ready APIs using FastAPI with validation, logging, and model loading

Implement drift detection using PSI and trigger automated retraining pipelines

Containerize applications using Docker and deploy scalable services on AWS ECR and EKS

Connect data, ML, MLOps, APIs, monitoring, and cloud into one cohesive system

Think like an architect and design production-first AI systems, not just models

Requirements

Basic understanding of Python programming

Familiarity with machine learning concepts (classification, features, evaluation metrics)

Basic knowledge of SQL is helpful but not mandatory

No prior MLOps or cloud experience required (covered step by step)

A system capable of running Python, Docker, and basic data processing workloads

Description

Software engineers who want to transition into AI/ML systems and production architecture,Machine learning practitioners who want to learn deployment, MLOps, and real-world pipelines,Backend developers interested in building AI-powered APIs and scalable services,Architects and senior developers who want to understand end-to-end AI system design,Anyone tired of isolated tutorials and wants to see how everything connects in production